Jobs Data Science

We are looking for a Senior AI/ML Engineer to join our team!

Our client is an innovative technology division within one of the world's top-10 copper producers. The team focuses on leveraging AI, machine learning, and advanced analytics to optimize industrial operations, improve asset reliability, and support data-driven decision-making across global mining and production facilities. The role is focused on building intelligent solutions for predictive maintenance, anomaly detection, and asset performance optimization, helping reduce downtime and improve operational efficiency across large-scale industrial environments.

Responsibilities:

• Design, develop, and deploy machine learning models for predictive maintenance, anomaly detection, and asset health monitoring.
• Build and maintain production-grade ML pipelines for time-series forecasting and sensor/telemetry data processing.
• Collaborate with Data Engineers and Analytics Engineers on data preparation, feature engineering, and model integration.
• Integrate model outputs, risk scores, and predictive insights into operational dashboards and business applications.
• Establish and maintain MLOps processes, including model deployment, monitoring, retraining, and drift detection.
• Research and evaluate new AI/ML approaches, tools, and technologies to improve predictive capabilities and business outcomes.
• Work closely with business and technical stakeholders to translate complex analytical results into actionable recommendations.

Requirements:

• 5+ years of experience as a Machine Learning Engineer or Data Scientist.
• Strong Python skills and hands-on experience with Pandas, NumPy, Scikit-Learn, SciPy, and related ML libraries.
• Proven experience with time-series forecasting and predictive modeling techniques (ARIMA, Prophet, XGBoost, LSTM/RNN, etc.).
• Experience working with telemetry, IoT, sensor, or equipment performance data.
• Hands-on experience with Azure Machine Learning, Databricks, MLflow, or similar MLOps platforms.
• Strong SQL skills and experience working with both relational and non-relational databases.
• Experience building and deploying production-ready ML solutions.
• Strong analytical and problem-solving skills with the ability to translate technical insights into business value.

Nice to have:

• Master's or PhD degree in Data Science, Statistics, Engineering, Mathematics, Operations Research, or a related quantitative field.
• Experience in mining, manufacturing, heavy industry, utilities, or other asset-intensive domains.
• Knowledge of Operational Technology (OT) environments and reliability engineering concepts.
• Experience with enterprise asset management systems such as SAP PM or IBM Maximo.
• Familiarity with reliability methodologies such as RCM or FMEA.
• Experience with real-time data processing and anomaly detection on streaming platforms (Kafka, Azure Stream Analytics, etc.).

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

*not applicable for freelancers

N-iX is a global software development company founded in 2002, connecting over 2,400+ tech professionals across 40+ countries. We deliver innovative technology solutions in cloud computing, data analytics, AI, embedded software,IoT, and more to global industry leaders and Fortune 500 companies. Join us to create technology that drives real change for businesses and people across the world. 

Our client is an innovative technology company developing advanced AI-powered solutions for enterprise and industrial environments. The organization focuses on leveraging artificial intelligence to optimize complex engineering, manufacturing, and supply chain processes through intelligent software platforms and data-driven decision-making.

The role supports two strategic AI initiatives aimed at transforming engineering and supply chain operations. The first program focuses on accelerating systems engineering lifecycles through NLP, Large Language Models, and automated compliance checking of complex technical and regulatory documentation. The second program is dedicated to predictive supply chain intelligence, including demand forecasting, spend analytics, supplier risk assessment, and anomaly detection. The technical environment includes enterprise ERP systems, engineering data models, Data Lakehouse infrastructure, and large-scale AI platforms integrated through RESTful services.

Responsibilities

• Design, develop, and deploy production-grade machine learning and AI solutions.
• Fine-tune and optimize Large Language Models (LLMs) for domain-specific use cases involving technical and regulatory documentation.
• Build NLP pipelines capable of extracting structured rules and business logic from unstructured text sources.
• Design and implement Retrieval-Augmented Generation (RAG) architectures for intelligent querying of large technical knowledge bases.
• Develop time-series forecasting models for spend prediction, demand planning, and supply chain optimization.
• Build machine learning models for supplier risk scoring, anomaly detection, and operational analytics.
• Create scalable data extraction, transformation, and feature engineering pipelines from structured and unstructured data sources.
• Collaborate with Data Engineers and Backend Engineers to integrate AI models into enterprise applications through RESTful APIs.
• Optimize model performance, scalability, and reliability for large-scale processing workloads.
• Validate model outputs, analyze model performance, and continuously improve prediction quality and accuracy.
• Work closely with Product Managers, Domain Experts, and Engineering teams to ensure business requirements are translated into effective AI solutions.

Requirements

• 5+ years of commercial experience in Data Science, Machine Learning, or Artificial Intelligence.
• Strong programming skills in Python.
• Hands-on experience with Pandas, NumPy, Scikit-learn, TensorFlow, and/or PyTorch.
• Strong experience with Natural Language Processing and transformer-based architectures such as GPT, BERT, Llama, or similar models.
• Experience with prompt engineering, fine-tuning, and implementation of Large Language Models.
• Proven experience designing and implementing Retrieval-Augmented Generation (RAG) solutions.
• Experience building and deploying machine learning models into production environments.
• Strong understanding of supervised and unsupervised machine learning techniques.
• Experience developing forecasting, classification, and predictive analytics models.
• Solid knowledge of statistics, probability theory, experimentation, and model evaluation methodologies.
• Experience working with SQL and/or NoSQL databases.
• Familiarity with complex data formats such as JSON and XML.
• Understanding of REST APIs and backend integration using frameworks such as Flask or FastAPI.
• Ability to quickly understand complex domain-specific terminology and translate business requirements into scalable AI solutions.
• Experience working within Agile or Sprint-based delivery environments.
• Strong analytical thinking, problem-solving, and communication skills.
• Upper-Intermediate or higher English level.

Nice to Have

• Experience with SAP S/4HANA or other ERP platforms.
• Experience working with Data Lakehouse architectures and large-scale enterprise data environments.
• Familiarity with specialized engineering data models or other specialized data structures.
• Experience with graph databases or graph-based data modeling.
• Knowledge of compliance automation, regulatory interpretation, or document intelligence solutions.
• Experience in manufacturing, industrial, supply chain, engineering, or other highly regulated industries.
• Exposure to MLOps practices and cloud-based AI/ML platforms.

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

*not applicable for freelancers

Preferred: Time-zone overlap with India working hours

Important: this is a Core AI R&D role requiring both strong applied ML engineering experience and research-level scientific depth. We are looking specifically for candidates with a research scientist mindset — someone who actively reads and publishes in relevant ML domains, understands state-of-the-art literature, and has demonstrated experience translating research ideas or papers into production-grade systems. Purely applied ML, analytics, or “model usage” backgrounds will not be a fit!
 

About the Opportunity

Our client is a fast-growing technology company developing advanced AI-powered solutions for industrial analytics and operational intelligence. As they continue to expand their Core AI Research & Development team, we are seeking a Senior Data Scientist with strong research and applied experience in one or more of the following areas:

• Time-series modeling for industrial sensor data
• Reinforcement learning and prescriptive decision-making
• Knowledge representation, graph learning, and multi-modal AI systems

This role offers the opportunity to work on cutting-edge AI research while driving real-world business impact through production-grade solutions.
 

Key ResponsibilitiesTime-Series Modeling

• Develop and enhance modern deep learning models for time-series analysis using large-scale industrial sensor datasets.
• Design retraining and adaptation pipelines to maintain model performance as data evolves over time.
• Apply transfer learning and low-label adaptation techniques to support new equipment types and sensor sources.
• Improve prediction accuracy across multiple asset categories.
   

Reinforcement Learning & Prescriptive Intelligence

• Design reinforcement learning frameworks that generate actionable operational recommendations.
• Develop optimization approaches that balance multiple business and operational constraints.
• Implement preference-learning techniques leveraging expert feedback and validated operational outcomes.
• Collaborate with product and engineering teams to integrate prescriptive recommendations into production environments.
   

Knowledge Representation & Multi-modal AI

• Expand and enhance domain knowledge graphs representing industrial assets, failure modes, and recommended actions.
• Apply graph-based learning methods to improve reasoning and knowledge transfer across equipment types.
• Integrate structured and unstructured data sources, including technical documentation, engineering diagrams, operator notes, and conversational data.
• Identify patterns and insights that improve model generalization across industries and customer segments.
   

Research & Collaboration

• Translate state-of-the-art research into scalable, production-ready solutions.
• Mentor junior data scientists and contribute to the growth of the research organization.
• Collaborate with cross-functional teams to bring AI innovations into customer-facing products.
• Define, monitor, and improve quality metrics for predictive and prescriptive AI systems.
• Contribute to patents, publications, and open-source initiatives where appropriate.
   

Required Qualifications, Education

• PhD preferred, or Master’s degree in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, or a related field.
• Exceptional candidates with equivalent industry experience will also be considered.
   

Technical Experience

• 5+ years of hands-on machine learning experience with deep expertise in at least one of:
  • Time-series modeling
  • Reinforcement learning
  • Graph machine learning and knowledge representation
• Strong Python programming skills.
• Experience with modern deep learning frameworks (PyTorch preferred).
• Practical knowledge of reinforcement learning methodologies and production-grade RL frameworks.
• Experience working with knowledge graphs, graph neural networks, embeddings, or related technologies.
• Familiarity with retrieval-augmented systems, vector databases, and unstructured data processing.
• Experience with cloud platforms and managed machine learning services.
• Strong software engineering practices, including version control, testing, and reproducible experimentation.
   

Soft Skills

• Strong product mindset with the ability to move research into production.
• Excellent communication skills and ability to work with both technical and non-technical stakeholders.
• Comfortable working in fast-paced environments with evolving priorities.
• Experience collaborating across distributed and international teams.

Nice to Have

• Experience in predictive maintenance, condition monitoring, industrial AI, or vibration analysis.
• Knowledge of physics-informed machine learning or causal inference techniques.
• Publications in leading ML conferences or journals.
• Experience with agentic AI systems, LLM evaluation, or advanced generative AI applications.
   

What Our Client Offers

• Opportunity to work on challenging AI research problems with direct business impact.
• Collaborative environment combining research excellence with product delivery.
• Remote-first culture with a globally distributed team.
• Exposure to large-scale industrial datasets and real-world AI applications.
• Competitive compensation package and long-term growth opportunities.
   

Looking forward to your reply!

Important: after confirming your application on this platform, you’ll receive an email with the next step: completing your application on our internal site, LaunchPod. So keep an eye on your inbox and don’t miss this step — without it, the process can’t move forward.

Why join us

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you! :)

About the role

We are looking for a Senior Data Scientist to develop predictive models and quantitative analytics that power risk-based vulnerability prioritization within a large-scale enterprise security program. You will structure and analyze massive security telemetry datasets, build ML algorithms to eliminate scanning noise and false positives, and establish unified data dashboards to drive adoption of data-driven security governance. The role requires 6+ years of data science experience applied specifically to cybersecurity, vulnerability management, or financial risk models.

What you will do

• Develop predictive models and quantitative analytics to prioritize vulnerabilities based on contextual risk and business impact;
• Structure and analyze massive outputs of security data from across the enterprise ecosystem to support the ASPM framework;
• Create machine learning algorithms or statistical models to automatically identify and filter out scanning noise and false positives;
• Establish a unified data delivery structure and dashboards to drive the adoption of data-driven security governance.

Must haves

• 6+ years of commercial data science experience, operating completely autonomously with no required supervision;
• Advanced proficiency in Python, R, and SQL;
• Experience with machine learning frameworks (TensorFlow, PyTorch) and data visualization tools;
• Prior commercial experience applying data science specifically to cybersecurity, vulnerability management, or financial risk models;
• Upper-intermediate English level.

Nice to haves

• Familiarity with Cloud Security Posture Management (CSPM) data outputs (e.g., Wiz).

The benefits of joining us

• Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps
• Competitive compensation: We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities
• A selection of exciting projects: Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands
• Flextime: Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.

Meet Our Recruitment Process

Asynchronous stage – An automated, self-paced track that helps us move faster and give you quicker feedback:

• Short online form to confirm basic requirements
• 30–60 minute skills assessment via Codility – a platform founded in Poland that helps us provide quicker feedback and streamline this stage of the process.
• 5-minute introduction video

Synchronous stage – Live interviews

• Technical interview with our engineering team (scheduled at your convenience)
• Final interview with your future teammates

If it’s a match — you’ll get an offer!

Cloudfresh ⛅️ is a Global Google Cloud Premier Partner, Zendesk Premier Partner, Asana Solutions Partner, GitLab Select Partner, Hubspot Platinum Partner, Okta Activate Partner, and Microsoft Partner.

Since 2017, we’ve been specializing in the implementation, migration, integration, audit, administration, support, and training for top-tier cloud solutions. Our products focus on cutting-edge cloud computing, advanced location and mapping, seamless collaboration from anywhere, unparalleled customer service, and innovative DevSecOps.

We’re currently looking for a Data Engineer/Analyst (GCP) to join our team. In this role, you’ll design, implement, and deliver robust data platforms and analytics solutions on Google Cloud - ensuring technical excellence and client success across the full project lifecycle, from pre-sales discovery to enterprise delivery and internal projects. We’re excited to meet someone who’s ready to make a real impact.

Requirements

- Fluency in Ukrainian
- At least B2 level in English
- GCP Native Data Stack: Deep practical knowledge of BigQuery, Cloud Storage, Cloud Functions, and BigQuery ML.
- 3+ years of experience in Data Engineering
- Strong experience with Dataflow, Dataproc, Pub/Sub, Cloud Composer, or Dataform for building and maintaining optimized ETL/ELT processes in GCP.
- Proficiency in Looker / Looker Studio.
- Advanced SQL (focus on optimization and performance tuning) and Python (for API integrations and pipeline scripting).
- Comfortable in client-facing and pre-sales scenarios (live demos, requirements gathering) with the ability to translate business needs into scalable technical designs.
- A proactive, self-driven approach with high responsibility for data platform reliability and accuracy.
- Familiarity with integrating data from APIs, relational databases, NoSQL systems, and SaaS platforms.
- Ability to work seamlessly with architects, DevOps, and ML engineers to deliver end-to-end cloud solutions.

Responsibilities

- Design and build modern data platforms for enterprise clients, working with both structured and unstructured datasets at scale.
- Lead the implementation of analytics solutions ensuring strict data quality, security, and rigorous performance optimization.
- Run workshops and deliver technical presentations to stakeholders to define and refine data architectures.
- Continuously monitor and tune BigQuery cost/scalability models and ETL performance.
- Collaborate with the team during discovery phases to provide technical expertise, live demos, and solution scoping.
- Identify and explore new GCP features and agentic AI tools to automate operations and improve process analytics.

Work conditions

- Flexible Work Format: Work remotely with flexible hours, allowing you to balance your professional and personal life efficiently.
- Training with Leading Cloud Products: Access in-depth training on cutting-edge cloud solutions, enhancing your expertise and equipping you with the tools to succeed in an ever-evolving industry.
- International Collaboration: Work alongside A-players and seasoned professionals in the cloud industry.
- Expand your expertise by engaging with international markets across the EMEA and CEE regions.
- Vibrant Team Environment: Be part of an innovative, dynamic team that fosters both personal and professional growth, creating opportunities for you to advance in your career.

When applying to this position, you consent to the processing of your personal data by CLOUDFRESH for the purposes necessary to conduct the recruitment process, in accordance with Regulation (EU) 2016/679 of the European Parliament and of the Council of April 27, 2016 (GDPR).
Additionally, you agree that CLOUDFRESH may process your personal data for future recruitment processes. You can withdraw this consent at any time.

Fozzy Group is one of the largest trade industrial groups in Ukraine and one of the leading Ukrainian retailers, with over 700 outlets all around the country. It is also engaged in e-commerce, food processing & production, agricultural business, parcel delivery, logistics and banking.
 

The Foodtech team is looking for a Data Scientist to develop the Operational Analytics function for a fast growing food delivery business. In this role, you will focus on time series forecasting, regression modeling, simulation modeling, and end-to-end machine learning pipelines to support resource planning and operational decision-making.

You will be responsible for developing simulation-based models that serve as a foundation for a digital twin of operational processes, enabling scenario analysis, stress testing, and what-if simulations for capacity planning and operational optimization.

You will work closely with product, engineering, and operations teams to transform data into measurable business impact through production-ready ML and simulation solutions.
 

Job Responsibilities:

• Develop and implement time series forecasting models for resource planning (demand, capacity, couriers, delivery slots, operational load);
• Build regression and machine learning models to explain key drivers and support operational decisions;
• Apply a wide range of time series approaches from classical models (SARIMA, ETS, Prophet) and ML models (GB) to modern Deep Learning models (LSTM, Temporal CNNs, Transformers for TS);
• Design, build, and maintain end-to-end automated ML pipelines, deploy and operate models in production using AWS SageMaker;
• Orchestrate training and inference workflows with Apache Airflow;
• Analyze large-scale operational datasets and convert results into insights, forecasts, and actionable recommendations;
• Collaborate with product managers, engineers, and operations teams to define business problems and validate analytical solutions;
• Monitor model performance, forecast stability, and business impact over time.
   

Requirements:

• Bachelor’s Degree in Mathematics / Engineering / Computer Sciences / Quantitative Economics / Econometrics;
• Strong mathematical background in Linear algebra, Probability, Statistics & Optimization Techniques;
• At least 2 years working experience on Data Science;
• Experience of the full cycle of model implementation (data collection, model training and evaluation, model deployment and monitoring);
• Ability to work independently, proactively, and to decompose complex problems into actionable tasks.
   

Skills:

• Strong proficiency in Python with solid application of object-oriented programming (OOP) principles (modular design, reusable components, maintainable code);
• Solid experience in time series forecasting and regression modeling;
• Practical knowledge of:o Classical and ML forecasting techniques;
  o Statistical methods (hypothesis testing, confidence intervals, A/B testing);
• Advanced SQL skills (window functions, complex queries);
• Experience building automated ML pipelines;
• Understanding of MLOps principles (model versioning, monitoring, CI/CD for ML).
   

Nice to have:

• Hands-on experience with AWS SageMaker (training jobs, endpoints, model registry);
• Experience with Apache Airflow for data and ML workflow orchestration;
• Knowledge of Reporting and Business Intelligence Software (Power BI, Tableau);
• Experience working with large-scale production data systems.
   

What We Offer:

• Competitive salary;
• Professional & personal development opportunities;
• Being part of dynamic team of young & ambitious professionals;
• Corporate discounts for sport clubs and language courses;
• Medical insurance package.

We’re looking for a Senior Machine Learning Engineer to design and scale production ML systems that power real-time personalization and decision-making at scale.

In this role, you’ll own the full ML lifecycle—from transforming raw behavioral data into meaningful features, to deploying low-latency prediction APIs, to building the observability needed to keep models reliable in production.

This is a great opportunity for someone with strong applied ML and MLOps expertise who enjoys solving complex engineering challenges and building scalable, high-impact systems.

Your responsibilities will include:

• Build and productionize ML models for ranking, personalization, and customer engagement.
• Develop pipelines that transform behavioral, demographic, and contextual signals into online and offline features.
• Design and deploy low-latency APIs and decision services for real-time decision-making.
• Implement experimentation frameworks, including A/B testing and exploration-exploitation strategies.
• Operationalize the ML lifecycle: automated training, CI/CD for models, artifact and feature versioning, and online/offline parity.
• Build observability into ML systems by monitoring data quality, model drift, and decision outcomes, and triggering retraining when needed.
• Establish closed feedback loops that connect decisions to business outcomes (e.g. conversions, engagement, fatigue signals such as unsubscribes).
• Collaborate closely with product and engineering teams to balance personalization, compliance, and business value in real-world systems.

What we expect from you:

• 5+ years of experience in applied ML engineering (recommendation systems, personalization, ranking, or advertising systems).
• Strong proficiency in Python or Go, SQL, and modern ML frameworks such as TensorFlow, PyTorch, or similar.
• Strong understanding of MLOps best practices, including CI/CD for ML, containerization (Docker), orchestration (Kubernetes, Airflow, Kubeflow), model registries, and monitoring frameworks.
• Familiarity with cloud ML platforms such as Vertex AI, SageMaker, or similar, and data warehouses like BigQuery, Snowflake, or Redshift.
• Experience deploying real-time ML systems, including low-latency serving, feature stores, and event-driven architectures.
• Understanding of multi-objective optimization and trade-offs in personalization systems.
• Comfort working cross-functionally in a dynamic startup environment with the overlap within USA time zone.
• Strong spoken and written English communication skills.

Nice to have:

• Experience in martech, adtech, CRM, or large-scale personalization platforms.
• Exposure to bandit algorithms, reinforcement learning, or causal inference for adaptive decision-making.
• Experience building systems serving millions of users at scale.
• Hands-on experience with Google Cloud Platform (GCP).
• Familiarity with observability tools such as Prometheus, Grafana, Evidently, WhyLabs, or Great Expectations for monitoring data and model health.

What we offer:

• Interesting projects and technical challenges that support both professional and personal growth.
• A long-term project with stability and impact.
• A flexible, results-oriented schedule with hybrid or remote work options.
• A comfortable, modern office in Kyiv with generator and battery backup.
• Competitive salary, medical insurance, and a supportive onboarding/trial period.
• Team-building events, including parties, online activities, picnics, and more.
• The opportunity to work in a Top Employer company (DOU 2025).

Equals5 helps pharmaceutical companies run precisely targeted ad campaigns on social platforms by identifying healthcare professionals and the consumer signals that reach them. The Identity team builds the data, scoring, and targeting systems that make this possible.

We are looking for a Data Analyst  to join the Identity team at Equals5. This is not a standard analyst role. We are building a dynamic identity resolution system where AI is deeply integrated — both as a productivity multiplier and as a core component of our data processing logic for identity resolution and LLM-based scoring.

We run enrichment against 70+ TB of data, apply AI and LLM-based scoring on top, and process around 20 million enrichment operations per day across hundreds of thousands of records.

The analytical challenges ahead are substantial: how to measure the quality of AI-driven scoring, how to validate that a change makes the system better, how to know where our money and time deliver the most value, and how to turn the questions our internal teams bring in into fast data-backed answers. You will own this side of the business, working daily alongside engineers and the product lead to turn these challenges into decisions that ship.

The main part of the role is not building dashboards — it is the data work that makes a meaningful dashboard possible in the first place. Concretely, you will be:

• Looking at data from multiple vendors side by side and deciding what to trust.
• Reconciling entities across sources that have different shapes and quality.
• Proposing and validating the logic that matches and scores them.
• Defining metrics and ground truth for questions nobody has asked before.
• Tracing why numbers don't line up across partial and sometimes contradictory sources.

Dashboards and visualizations are part of how findings get communicated, but they come at the end — the surface of the work, not its core. Most of your time is spent on what sits underneath: methodology, judgment calls on data quality, and shaping how the domain understands its own numbers.

Responsibilities

• Measurement & Evaluation Foundations: Define how we measure what matters — data quality, the accuracy of our AI scoring, how many records actually get matched to usable signals, cost per outcome. Build the feedback loops that tell us whether a change makes the system better or worse.

• Scoring Intelligence (co-owned with engineers): Partner with the engineering team on the LLM-based scoring system. You will build the evaluation framework for AI scoring, run experiments comparing prompts, models, and approaches against a baseline, and make data-backed calls on where we invest next. Our AI scoring system is actively evolving — you will shape how we measure and improve it.

• Client-related Analysis: Own the analytical side of the work that internal sales and media-buying teams bring in from pharma clients — how reachable a target audience is, how good the data looks, why a campaign performed the way it did. You turn their questions into data-backed answers they can use. Identity is a product core; the analyst works with internal teams, not with clients directly.

• Cost and Value Analysis: Turn cost and value into product decisions. Which data sources and enrichment scenarios pay off, where we overspend, what to cache, what to cut. We operate a mix of free owned data and paid external providers — the decisions have real dollar impact.

• Active AI Usage: Use Claude Code and modern AI engineering tools to multiply your own output. Automate the repetitive parts of analysis so you can focus on the decisions. We provide Claude Code licenses and expect active daily use.

• Data Quality Response: When pipeline output drifts, a provider degrades, or upstream data shifts, you notice first, diagnose the root cause, and drive the fix. You treat data quality as a product, not a ticket queue.

Requirements

• 5+ years as a Data Analyst, Analytics Engineer, or Data Scientist in a product team (not consultancy, not BI-only roles).
• Advanced SQL at TB scale — BigQuery, Snowflake, or equivalent warehouse. You can read and write complex queries on tens of terabytes without someone pre-modelling the data for you.
• Python for analysis at a working level — pandas, Jupyter, or modern equivalents. Comfortable putting together a small script or helper when an existing tool doesn't cover your case.
• Built measurement or evaluation frameworks from scratch for systems that had no baseline. You are comfortable defining what and how to measure, not consuming someone else's KPI.
• Comfort with probabilistic systems. You understand that noise is inherent and 100% accuracy is not the goal — scoring, matching, ranking.
• AI-native workflow. You already use Claude Code, ChatGPT, Copilot, or similar AI tools daily and integrate them into your analytical process.
• English: B2+ (Upper-Intermediate) or higher.

Strong plus 

• LLM evaluation experience — A/B testing AI-powered features, output scoring, prompt iteration with measurable outcomes.
• Identity resolution / identity graph experience — adtech, martech, or customer data platform context (LiveIntent, LiveRamp, Epsilon, Experian Marketing, Acxiom, Merkle, or similar).
• Healthcare or pharma data — working with NPI, HCP-level data, healthcare taxonomies, pharma targeting.

Nice to have

• Experience running controlled experiments on data pipelines.
• Prompt engineering or structured LLM prompt design.
• Prior work in AI-first product companies.

What We Offer

• Fully remote with flexible hours, with working overlap in CET ±2 hours for syncs.
• AI-Native Environment: We provide licenses for Claude Code and encourage using the bleeding edge of AI tech — both for daily work and as part of product logic.
• High-Impact Role: You will directly shape how we identify, score, and reach healthcare professionals — the core business of our domain.
• Small team, direct impact: You will work directly with Product and Engineering leads — no layers between you and the people making decisions.
• No Bureaucracy: Fast decisions, no legacy processes, focus on results.

Workload: minimum 50%, up to 80–100% !!!
Start: after summer holiday period (early autumn)
Duration: long-term assignment (exact length to be confirmed)
Location: flexible / hybrid (EU-based collaboration expected)
English: B2


Description:
We are looking for an experienced Senior Data Architect to define a reusable and scalable data foundation supporting sustainability, EHS, and product compliance use cases within a global manufacturing environment.

This assignment focuses on building a governed data architecture that enables automation of lifecycle-related calculations and reporting (such as LCA, product carbon footprint, and environmental declarations), while also supporting broader sustainability reporting, regulatory compliance, and future digital product requirements.

Responsibilities:

• Define the target data architecture and reusable data foundation
• Identify and structure key data domains and data products
• Design data ownership and governance models
• Establish principles for:
• Data quality and validation
• Data lineage and traceability
• Versioning and auditability
• Ensure a modern lakehouse platform acts as the governed backbone for trusted data
• Enable integration of external calculation and authoring tools where appropriate
• Collaborate across business and IT domains to avoid fragmented point-to-point integrations
• Support the creation of reusable, auditable data products for multiple stakeholders

Required experience !!!!!:

• Strong experience in data architecture and modern data platforms (lakehouse)
• Hands-on knowledge of data governance, data products, and domain-driven data design
• Experience working with manufacturing, product data, or industrial environments
• Understanding of sustainability reporting and/or compliance-driven data requirements
• Ability to work across multiple stakeholders: business, IT, data, and compliance teams
• Experience integrating data across systems such as ERP, PLM, and analytics platforms

Nice to have:

• Experience with sustainability-related domains (e.g. lifecycle assessment, carbon footprint, environmental reporting)
• Experience with compliance-driven data (e.g. chemical, product, EHS)
• Familiarity with emerging requirements such as Digital Product Passport

We are looking for an experienced Senior Data Scientist to join our team and help unlock the full value of data across our platform, products, and business operations. In this role, you will apply advanced analytics, machine learning, and statistical modeling to generate actionable insights, improve decision-making, and drive innovation across the organization.

Responsibilities

• Analyze large, complex datasets to uncover trends, patterns, and insights that support strategic business and product decisions.
• Build predictive models to forecast business outcomes, improve operational efficiency, and support decision-making across multiple functions.
• Design and implement feature engineering techniques to improve machine learning model performance and accuracy.
• Partner with cross-functional teams including product, engineering, operations, and business stakeholders to identify opportunities and translate them into scalable data science solutions.
• Clean, preprocess, and structure data from multiple sources to ensure high-quality inputs for analysis and modeling.
• Develop, test, and deploy machine learning models for use cases such as classification, regression, clustering, recommendation systems, and forecasting.
• Evaluate and optimize model performance using appropriate statistical and machine learning metrics.
• Create dashboards, reports, and visualizations that clearly communicate findings and recommendations to technical and non-technical audiences.
• Automate recurring analysis, reporting, and model workflows to improve efficiency and consistency.
• Monitor production models and continuously improve performance, reliability, and business impact over time.
• Stay current with the latest advancements in data science, machine learning, and AI, and apply relevant innovations to PredictSpring’s platform and operations.

Requirements

• Excellent communication skills, including fluent verbal English, with the ability to explain complex concepts to non-technical stakeholders and collaborate effectively across teams.
• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Proven experience as a Data Scientist, with a strong background in machine learning, statistical analysis, and predictive modeling.
• Strong programming skills in Python preferred, or similar languages such as R, with hands-on experience using libraries such as Pandas, NumPy, scikit-learn, TensorFlow, or PyTorch.
• Expertise in building and deploying machine learning models for classification, regression, time-series forecasting, recommendation systems, and related applications.
• Strong experience with data wrangling, feature engineering, and working with structured and unstructured datasets.
• Proficiency in SQL for querying, transforming, and analyzing structured data.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud for model deployment and data workflows.
• Experience with data visualization tools such as Power BI, Tableau, or Python visualization libraries.
• Strong analytical thinking, problem-solving ability, and a passion for applying data science to real-world business challenges.

Preferred Qualifications

• Familiarity with big data and distributed processing tools such as Apache Spark, Hadoop, or Databricks.
• Experience with deep learning frameworks such as TensorFlow, Keras, or PyTorch.
• Experience with natural language processing techniques such as text classification, sentiment analysis, or large language model applications.
• Strong foundation in statistical analysis, experimentation, and hypothesis testing.
• Experience collaborating with or supporting data engineering teams in building scalable data pipelines.
• Familiarity with version control tools such as Git.
• Strong business acumen and the ability to align technical solutions with business goals.
• Experience in retail, e-commerce, omni-channel commerce, or POS technology is a strong plus.

Why PredictSpring?

• Join a market-leading company shaping the future of omni-channel retail and modern POS.
• Work on high-impact challenges at the intersection of commerce, data, and customer experience.
• Collaborate with a talented global team in a flexible remote work environment.
• Enjoy opportunities for growth, learning, and professional development.
• Contribute to products used by some of the world’s most recognized retail and lifestyle brands.
• Be part of an innovative, collaborative, and data-driven culture focused on delivering measurable business value.

Quantum is a global technology partner delivering high-end software products that address real-world problems. 

We advance emerging technologies for outside-the-box solutions. We focus on Machine Learning, Computer Vision, Deep Learning, GIS, MLOps, Blockchain, and more.

Here at Quantum, we are dedicated to creating state-of-the-art solutions that effectively address the pressing issues faced by businesses and the world. To date, our team of exceptional people has already helped many organisations globally attain technological leadership.

We constantly discover new ways to solve never-ending business challenges by adopting new technologies, even when there isn't yet a best practice. If you share our passion for problem-solving and making an impact, join us and enjoy getting to know our wealth of experience!

About the position

Quantum is expanding its team in Central Europe and is looking for talented Data Science Engineers to join us. Our projects span multiple domains, including data analysis, fintech, computer vision, image processing, edge-device solutions, Large Language Models (LLMs), and classical Machine Learning applications. We build custom Data Science solutions for clients across various industries, combining strong engineering practices with a research-driven mindset.

Joining Quantum means becoming part of a team that values solid technical fundamentals, continuous learning, and practical problem-solving. You'll collaborate on impactful products, work with cutting-edge technologies, and contribute to projects that turn complex ideas into real-world solutions.

Must have skills:

• 3+ years of commercial DS/ML experience
• Solid foundation in linear algebra, statistics, and probability
• Strong Python, including NumPy, Pandas, and scikit-learn
• Hands-on experience with PyTorch (or TensorFlow)
• Deep experience in at least one of: 
  • Computer Vision (modern DL: detection, segmentation, classification - ViT, YOLO, SAM); 
  • LLMs / NLP (fine-tuning, RAG, prompt engineering, embeddings, evaluation); 
  • Classical ML at scale (tabular, time series, recommender systems)
• Working knowledge of Transformers and attention; familiarity with CNNs
• Experience deploying models to production (Docker, REST/gRPC serving, basic monitoring)
• Experience with at least one cloud platform (AWS, GCP, or Azure)
• Clean code and Git workflow
• Experience with SQL
• Upper-Intermediate English (B2+) - you'll communicate directly with clients

Nice to have skills:

• Cross-domain exposure: if your main strength is CV, some LLM exposure is a plus (and vice versa)
• Edge deployment: TensorRT, OpenVINO, ONNX, quantisation, model distillation, mobile/embedded inference
• LLM tooling: LangChain/LlamaIndex, vector databases (pgvector, Pinecone, Weaviate, Qdrant), LoRA/QLoRA fine-tuning, agentic frameworks
• MLOps & model monitoring: MLflow, DVC, Weights & Biases, CI/CD for ML
• Classical CV with OpenCV (still relevant for hybrid pipelines and edge work)
• Ability to read recent papers and implement methods in new domains
• Experience working with external clients
• Participation in Kaggle competitions

Your tasks will include:

• Own full-cycle DS projects: scoping with the client, data prep, modelling, evaluation, deployment
• Build solutions across CV, LLM/NLP, and classical ML depending on the project pipeline
• Deploy models to production - cloud, edge, or hybrid, depending on client needs
• Read recent research and adapt methods to client problems
• Communicate technical decisions and trade-offs directly to clients

We offer:

• Delivering high-end software projects that address real-world problems
• Surrounding experts who are ready to move forward professionally
• Professional growth plan and team leader support
• Taking ownership of R&D and socially significant projects
• Participation in worldwide tech conferences and competitions
• Taking part in regular educational activities
• Being a part of a multicultural company with a fun and lighthearted atmosphere
• Working from anywhere with flexible working hours
• Paid vacation and sick leave days

Join Quantum and take a step toward your data-driven future.

Workload: minimum 50%, up to 80–100% !!!
Start: after summer holiday period (early autumn)
Duration: long-term assignment (exact length to be confirmed)
Location: flexible / hybrid (EU-based collaboration expected)
English: B2


Description:
We are looking for an experienced Senior Data Architect to define a reusable and scalable data foundation supporting sustainability, EHS, and product compliance use cases within a global manufacturing environment.

This assignment focuses on building a governed data architecture that enables automation of lifecycle-related calculations and reporting (such as LCA, product carbon footprint, and environmental declarations), while also supporting broader sustainability reporting, regulatory compliance, and future digital product requirements.

Responsibilities:

• Define the target data architecture and reusable data foundation
• Identify and structure key data domains and data products
• Design data ownership and governance models
• Establish principles for:
• Data quality and validation
• Data lineage and traceability
• Versioning and auditability
• Ensure a modern lakehouse platform acts as the governed backbone for trusted data
• Enable integration of external calculation and authoring tools where appropriate
• Collaborate across business and IT domains to avoid fragmented point-to-point integrations
• Support the creation of reusable, auditable data products for multiple stakeholders

Required experience !!!!!:

• Strong experience in data architecture and modern data platforms (lakehouse)
• Hands-on knowledge of data governance, data products, and domain-driven data design
• Experience working with manufacturing, product data, or industrial environments
• Understanding of sustainability reporting and/or compliance-driven data requirements
• Ability to work across multiple stakeholders: business, IT, data, and compliance teams
• Experience integrating data across systems such as ERP, PLM, and analytics platforms

Nice to have:

• Experience with sustainability-related domains (e.g. lifecycle assessment, carbon footprint, environmental reporting)
• Experience with compliance-driven data (e.g. chemical, product, EHS)
• Familiarity with emerging requirements such as Digital Product Passport

We are looking for a Computer Vision Engineer with strong experience in deep learning and production computer vision systems.

The role involves developing and bringing computer vision models into production, covering the full lifecycle from training and evaluation to deployment and system integration.

Responsibilities:

• Develop and improve Computer Vision and Machine Learning models
• Design and implement solutions for object detection, classification, segmentation, and tracking
• Work with large-scale image, video, and telemetry datasets
• Prepare, process, and maintain datasets for model training and evaluation
• Manage model training, validation, and testing workflows
• Optimize models for production deployment and real-time performance
• Integrate ML models into software services, applications, and APIs
• Analyze model performance and identify opportunities for quality improvement
• Participate in the design and architecture of AI components within the product ecosystem
• Collaborate closely with Backend, Infrastructure, Data, and Product Engineering teams
• Contribute to the development of MLOps practices and the end-to-end machine learning lifecycle.

Requirements:

• 3+ years of experience in computer vision
• High proficiency in Python
• Strong experience with deep learning and the PyTorch framework for computer vision tasks
• Research experience in computer vision with a focus on delivering results
• Ability to work independently or within a small team, with a strong sense of ownership over results

Nice to Have:

• Experience working with thermal imaging data (thermal cameras, infrared imagery) would be an advantage
• Experience with deep learning solutions for detection, tracking, image retrieval, and segmentation tasks
• Understanding of geometric computer vision principles
• Model optimization: quantization, pruning, neural network compilers
• Hands-on experience implementing low-level CV algorithms
• Experience with sensor fusion (IMU, magnetometer, GNSS, camera)
• Knowledge of C++

We are looking for a Computer Vision Engineer with strong experience in deep learning and applied computer vision systems.
The role involves working on challenging CV problems in real-world environments.
We consider Middle and Senior engineers, with scope adapted to experience.
Responsibilities

• Develop, train, and optimize deep learning models for:
  • image retrieval
  • image matching (keypoint detection and matching)
  • auxiliary perception tasks supporting the main pipeline
• Evaluate models using quantitative metrics, including:
  • retrieval quality (mAP, Recall@K)
  • matching performance (precision/recall, repeatability)
  • end-to-end system metrics (accuracy, latency, robustness)
• Optimize models for production deployment using modern toolchains:
  • ONNX / TensorRT / OpenVINO / edge acceleration where applicable
  • model compression techniques (quantization, pruning, distillation)
  • latency, memory, and throughput optimization
• Work with large-scale visual datasets and descriptor-based representations
• Collaborate with engineering teams to integrate models into production systems

Required Qualifications

• 2–4+ years of experience in Computer Vision / Deep Learning
• Hands-on experience with keypoint detection and matching models (e.g. SuperPoint, R2D2, DISK, LightGlue, SuperGlue)
• Experience with image retrieval or metric learning systems
• Strong understanding of geometric and motion-related computer vision concepts:
  • keypoint detection and description
  • image matching and geometric verification (RANSAC, homography, PnP)
  • pose estimation and refinement techniques (PnP, bundle adjustment, pose graph optimization)
  • optical flow and frame-to-frame tracking
  • vector search / ANN methods for descriptor retrieval
• Strong Python skills (PyTorch and scientific computing stack)
• Ability to read and understand inference code in C++

Nice to Have

• Experience with noisy or imperfect real-world datasets
• Self-supervised or unsupervised learning methods (contrastive learning, homography supervision, etc.)
• Experience optimizing models for edge deployment (quantization, pruning, distillation)
• Familiarity with FAISS or similar vector search systems
• Experience with optimization libraries for geometric problems (bundle adjustment, pose refinement)
• Understanding of real-time constraints (latency, memory, CPU inference budgets on ARM or low-power x86 systems)

We are looking for Computer Vision Engineers with strong experience in classical and geometric computer vision, and hands-on experience with SLAM, Visual-Inertial Odometry (VIO), or related localization systems.

We are open to candidates of different seniority levels (Junior to Senior), with responsibilities and scope defined by experience.

Responsibilities:

• Develop and optimize classical and geometric computer vision algorithms
• Work with feature tracking, matching, and geometric processing
• Contribute to SLAM / VIO pipeline components
• Integrate and test multi-sensor fusion systems (camera + IMU)
• Design and run experiments, evaluate algorithm performance
• Translate research papers into practical implementations
• Collaborate with cross-functional engineering teams

Required Qualifications:

• Hands-on experience with classical computer vision
• Understanding of geometric computer vision concepts, including camera models, feature detection, and tracking
• Familiarity with SLAM and/or Visual-Inertial Odometry (VIO) concepts
• Experience with C++ and Linux environments
• Ability to design, implement, and optimize computer vision algorithms in practice
• Ability to learn from and implement research-based methods
• Degree or ongoing studies in Computer Science, Mathematics, Physics, Engineering, or a related STEM field

Nice to Have:

• Experience with Python
• Knowledge of neural networks and CV libraries/frameworks (OpenCV, NumPy, PyTorch, ONNX, Eigen, etc.)
• Experience with sensor fusion techniques
• Familiarity with embedded or real-time systems
• Robotics or navigation background