Jobs Data Science

We are looking for a Computer Vision Engineer to design and implement visual intelligence systems that power personalized digital experiences across connected devices and mobile applications.

This is a hands-on role focused on applied computer vision - from model design and training to on-device deployment. You will work at the intersection of deep learning, image and video processing, and product integration, collaborating closely with backend engineers, ML engineers, and product managers to bring vision-driven features into production and continuously optimize them for real-world usage and resource-constrained environments.

Responsibilities:

• Design, train, and deploy computer vision models for use cases such as object detection, classification, segmentation, and visual analysis;
• Build and maintain data pipelines for image and video processing, including annotation, augmentation, and dataset versioning workflows;
• Optimize models for inference on edge and mobile devices (quantization, pruning, distillation, hardware-accelerated runtimes);
• Collaborate with product and engineering teams to translate product ideas into vision-powered features;
• Implement model observability, monitoring, and retraining workflows for models running in production;
• Contribute to the architecture of a modern ML platform within a cloud-native microservices environment;
• Work with backend and mobile teams to expose model outputs via APIs and integrate them into customer-facing experiences;
• Evaluate emerging CV/AI research and assess promising methods for future implementation.

Requirements:

• 3+ years of experience in applied computer vision or machine learning with a strong CV focus;
• Strong knowledge of:
  • Deep learning for vision: CNNs, object detection and segmentation architectures (e.g. YOLO, Faster R-CNN, U-Net), and modern vision transformers;
  • Python and ML libraries such as PyTorch, TensorFlow, OpenCV;
  • Building, deploying, and maintaining CV models in production.
• Familiarity with:
  • Model optimization and on-device inference (ONNX, TensorRT, TensorFlow Lite, Core ML, OpenVINO);
  • MLOps tools (e.g. MLflow, TFX, Airflow, Vertex AI);
  • Data and video pipelines (e.g. Kafka, BigQuery, FFmpeg, GStreamer);
  • Model serving frameworks (e.g. FastAPI, TensorFlow Serving, TorchServe).
• Experience with cloud platforms (GCP, AWS, or Azure) and container-based deployments;
• Comfortable collaborating with product, engineering, and analytics teams;
• Bonus: Experience with edge AI, mobile ML, multi-modal models, or real-time video processing at scale.

We Offer:

• Remote work and flexible working hours.
• Legal support.
• Paid sick leaves.
• Paid vacations.
• Medical insurance. 
• Free English classes.

We are looking for a Game Mathematician to work on slot games within an iGaming product. This role is for someone who can turn mathematics into an engaging and well-balanced gaming experience, works confidently with probabilities, RTP, and simulations, and collaborates effectively with developers and QA.

Responsibilities
• Design and calculate mathematical models for slot games (RTP, volatility, hit rate, max win).
• Build and balance core game elements: paytables, reels / weights, wild & scatter logic, free spins, respins, multipliers, bonus features.
• Prepare complete mathematical documentation for implementation: formulas, rules, tables, edge cases, configurations.
• Run and analyze Monte Carlo simulations to validate RTP, payout distribution, and volatility.
• Prepare math validation reports.
• Support developers and QA during integration and testing phases.

Optional / Additional Responsibilities
• Research iGaming trends and competitor mechanics.
• Participate in the full game lifecycle (from concept to release).
• Analyze player behavior using mathematical and statistical data.

Requirements
• Commercial experience in slot math / casino game mathematics.
• Strong understanding of probability theory, combinatorics, and statistics.
• Clear understanding of RTP structure (base vs bonus contribution), volatility management, and feature triggers.
• Ability to produce clear and well-structured technical documentation for engineering teams.
• Strong analytical thinking, attention to detail, and ownership of results.

Nice to Have
• Confident knowledge of Python.
• Experience building simulation scripts / tools (config-driven, reproducible simulations, result export).
• Experience with jackpot / progressive models.
• Understanding of compliance and regulatory requirements in iGaming.

Type: Full-time, remote
Location: EU + CIS + Balkans (citizenship doesn't matter — what matters is current location and the jurisdiction from which access to client servers would occur)
Contract: 12 months, long-term, with potential for extension
Start date: Early September 2026 (ASAP)
Time zone: CET
English: B2 minimum, C1 preferred

About the Role

We're looking for a senior Data Architect to design and modernize an Azure data platform, including the data architecture underpinning GenAI/RAG/Agentic applications. This is an architecture role, not hands-on RAG/ML development — the focus is on how enterprise data and knowledge are ingested, prepared, stored, governed, and made available for retrieval and AI consumption.

Must-Haves

• Azure Cosmos DB — hands-on experience with data modeling, partitioning, indexing, RU/throughput and cost optimization, consistency, Change Feed
• Proven experience designing data platforms supporting GenAI/RAG use cases
• AI-ready data architecture experience
• A real project where the candidate designed or contributed to the data architecture supporting a RAG, GenAI, or Agentic AI application
• Azure Synapse Analytics — depth across workload patterns (Pipelines, Spark, Serverless SQL, Dedicated SQL Pools)
• Azure Databricks
• Azure SQL MI / SQL Server / T-SQL
• Azure Data Lake / Lakehouse architecture
• Azure Functions
• Experience with structured and genuinely unstructured data (documents, PDFs, scans, images, email, free text)
• Metadata/configuration-driven ingestion — reusable, scalable design for onboarding new data sources
• Demonstrated architecture ownership — real decisions made, not just pipeline implementation
• At least 4 years of recent (not dated) hands-on Azure experience
• Must be an Architect by role, not a Data Engineer

What You'll Do

• Take over an existing, problematic data platform, quickly assess it, and deliver both tactical fixes and a longer-term target architecture
• Design a reusable ingestion architecture for heterogeneous sources (databases, APIs, files)
• Make technology-neutral calls on when Databricks/Synapse/ADF are actually needed — and when they're not
• Produce detailed, implementation-ready architecture documentation for engineering teams
• Own data governance, data quality, and incremental processing
• Communicate effectively with both technical and business stakeholders

A Note on GenAI/RAG

You won't be building RAG solutions or AI agents yourself. What's required is experience designing the data architecture around such applications — how enterprise data and knowledge get ingested, processed, indexed, and made available for retrieval and AI consumption (document processing, OCR, chunking, embeddings, vector search, etc.), including where Cosmos DB fits into that picture.

Interview Process

1. Intro call
2. Technical interview (~1 hour)

Be ready to walk through 2–3 concrete project examples using: problem → options considered → your decision → rationale → implementation → result. The focus is less on definitions and more on how you think as an architect and how quickly you can turn a messy existing situation into practical direction for an engineering team.

We are looking for a Middle AI/ML Engineer to develop and enhance AI-driven solutions within the Palantir Foundry and AIP ecosystem.

In this role, you will focus on building and iterating on machine learning and LLM-based solutions, integrating them into Foundry workflows to support analytics, automation, and decision-making. You will collaborate closely with data engineers, business analysts, and domain experts to deliver practical, production-ready AI solutions.

Responsibilities:

• Develop and enhance machine learning and AI models to support predictive analytics, classification, forecasting, and AI-assisted workflows.
• Build AI and ML solutions within Palantir Foundry, using Python and existing Foundry pipelines, Ontology objects, and workflows.
• Apply LLMs and NLP techniques (e.g. prompt engineering, fine-tuning, embeddings, retrieval-augmented workflows) using Palantir AIP for enterprise use cases.
• Collaborate with data engineers to understand data sources, ensure data quality, and prepare datasets for model training and inference.
• Conduct experiments, evaluate model performance, and iterate on features and model approaches.
• Integrate AI models into Foundry workflows to surface insights and support business processes.
• Support model deployment and monitoring by following established team standards and best practices.
• Work closely with business and domain stakeholders to translate requirements into practical AI-driven solutions.
• Document model behavior, assumptions, and limitations to support transparency and compliance.
• Stay up to date with applied AI and GenAI trends and contribute ideas under guidance from senior team members

Requirements:

• 3+ years of experience in machine learning, AI engineering, or applied data science.
• Strong Python skills; experience with ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Practical experience with RAG architectures, vector databases, and retrieval strategies
• Hands-on experience with LLMs, NLP, or GenAI use cases (e.g. prompt design, embeddings, text classification, summarization).
• Practical understanding of the ML lifecycle: data preparation, feature engineering, model training, evaluation, and iteration.
• Experience working with structured data (tabular, time series); exposure to text or unstructured data is a plus.
• Familiarity with enterprise data environments and collaborative development workflows.
• Ability to clearly explain model results and AI behavior to non-technical stakeholders.
• Upper-Intermediate English or higher.

Nice to have: 

• Proficiency in Foundry Ontology, Object Builders, and Code Repositories.
• Experience in big pharma or highly regulated industries.
• Knowledge of data privacy, compliance, and security best practices in AI applications.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).

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

We are looking for a Senior AI/ML Engineer to design, develop, and enhance AI-driven solutions within the Palantir Foundry and AIP ecosystem.

In this role, you will take ownership of developing production-ready machine learning and LLM-based solutions, from understanding business needs and exploring data to designing technical approaches, implementing solutions, and integrating them into Foundry workflows. You will work closely with data engineers, business analysts, and domain experts, contribute to technical decisions, and help establish best practices for building scalable and reliable AI solutions.

Key Responsibilities:

• Design and develop AI/ML solutions for predictive analytics, classification, forecasting, automation, and AI-assisted workflows within Palantir Foundry, leveraging Python, Ontology objects, pipelines, and workflows.
• Design, implement, evaluate, and optimize LLM/NLP and RAG solutions using Palantir AIP, including prompt engineering, embeddings, retrieval strategies, vector search, and text processing.
• Own the end-to-end lifecycle of AI solutions, from experimentation and model development to deployment, monitoring, and continuous improvement.
• Collaborate with data engineers to understand data sources, ensure data quality, and design efficient and scalable data processing approaches.
• Integrate AI solutions into production workflows and optimize them for accuracy, reliability, latency, scalability, and cost-efficiency.
• Contribute to technical architecture and design decisions, including model, framework, and tool selection; proactively identify technical risks, improvement opportunities and proactively propose solutions.
• Work with business and domain stakeholders to translate business needs into practical, scalable, and production-ready AI solutions, clearly communicating technical decisions and model limitations.
• Identify technical risks, limitations, and opportunities for improvement and proactively propose solutions.

Requirements:

• 4+ years of experience in machine learning, AI engineering, applied data science, or related fields.
• Strong Python skills and hands-on experience with ML frameworks such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Strong practical experience with LLMs, NLP, or GenAI applications, including prompt engineering, embeddings, text processing, summarization, or similar use cases.
• Solid hands-on experience designing and implementing RAG architectures, vector databases, and retrieval strategies.
• Strong understanding of the ML lifecycle, including data preparation, feature engineering, model development, evaluation, deployment, and monitoring.
• Experience working with both structured and unstructured data, including tabular data, time series, text, and documents.
• Ability to independently analyze ambiguous problems, propose technical approaches, and drive solutions from concept to implementation.
• Strong communication skills, with the ability to explain AI concepts, technical decisions, and model behavior to non-technical stakeholders.
• Upper-Intermediate English or higher.

Nice to Have:

• Proficiency in Foundry Ontology, Object Builders, and Code Repositories.
• Experience in big pharma or highly regulated industries.
• Knowledge of data privacy, compliance, and security best practices in AI applications.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)

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

We are looking for a Computer Vision / C++ Engineer to join our team and work on real-world computer vision systems.

Responsibilities:
 

- Develop and maintain computer vision solutions in C++;

- Work with geometric computer vision (projective and epipolar geometry);

- Implement classical CV algorithms (demosaicing, tracking, optical flow, color calibration, image enhancement);

- Fine-tune existing deep learning models (e.g. YOLO) and train custom models using PyTorch;

- Deploy DL models in C++ using inference runtimes (ONNX Runtime, OpenCV DNN or similar);

- Implement model output decoding and post-processing;

- Develop camera-related software;

- Write efficient multi-threaded code.

Requirements:
 

- Strong C++ skills;

- Solid background in computer vision;

- Practical experience with deep learning for CV and PyTorch;

- Experience with deploying models to C++ applications and inference runtimes;

-Experience with multi-threaded programming and camera systems.

We are looking for a Senior Data Scientist

Responsibilities:

• Analyze large and complex data to discover hidden patterns and opportunities;
• Develop and own end-to-end highly scalable data pipelines;
• Develop state-of-the-art machine learning models, and algorithms and build solutions in monetization fields;
• Provisioning data models for real-time (online) processing;
• Shape new data products, and constantly improve the existing products;
• Collaborate with multiple teams including Product, BI, Analysts, DevOps, R&D and Marketing to lead and accomplish overall solutions;
• Oversee a complete release to production, including analyzing requirements, testing, monitoring and measuring results, multitasking, and quickly responding to critical issues.
  
  Requirements:
• B.Sc./M.A/M.Sc. degree in Computer Science, Engineering, Math, Statistics, or other equivalent fields;
• 6 years as a Data Scientist - a strong background in ML concepts and models;
• Hands-on experience with Python, especially with data science ML libraries (regression& classification algorithms e.g. XGB or LGBM, recommendation system algorithms, etc.;
• Understanding of databases and SQL for data retrieval;
• Monitoring and alerting tools (e.g. Grafana, Kibana).
   

Will be a plus:

• Knowledge in the programmatic advertising landscape – DSPs, SSPs, exchanges – significant advantage;
• Experience with relational (e.g. Vertica, VoltDB) and non-relational (e.g. MongoDB) databases;
• Experience with PMML.

Duration: long-term

We are looking for a Middle Data Scientist with strong hands-on experience in time-series, deep learning, and Python development.

This role is a good fit for someone with 3+ years of professional experience in Data Science or Machine Learning, a strong understanding of neural networks, and practical experience developing predictive models for time-series and tabular data.

We expect solid knowledge of different neural network architectures, including recurrent and convolutional networks, as well as practical experience designing, training, tuning, validating, and deploying models.

Strong Python programming skills are essential for this position. You should also be confident working with statistics, data analysis, and production-oriented Data Science code.

What you will work on:

• Perform exploratory data analysis and prepare statistical summaries for hypothesis testing and data-driven decision-making.
• Build data preparation and feature engineering pipelines used by predictive models.
• Design and implement predictive modelling approaches.
• Continuously analyse and improve models already running in production.
• Take an active role in Python development related to end-to-end ML model delivery and support internal ML-related libraries.
• Tune loss functions, evaluation metrics, sample weights, hyperparameters, and other model components to improve model quality.

• Prepare detailed model evaluation reports, summarise findings, and communicate practical insights to stakeholders.

   

What we expect from you:

• At least 3 years of professional experience working as a Data Scientist or Machine Learning Engineer.
• Practical experience building custom neural networks with PyTorch.
• Strong knowledge of neural network architectures applicable to time-series forecasting and predictive modelling of tabular data.
• Experience working with layers such as LSTM, GRU, Conv1D, Dense, Dropout, and BatchNormalization, as well as activation functions including ReLU, Sigmoid, Tanh, and Softmax.
• Hands-on experience working with time-series datasets, including data loading, cleaning, preparation, and feature engineering with pandas and NumPy.
• Experience with the Darts library would be a significant advantage.
• Experience selecting and developing models available in Darts, as well as creating custom neural network architectures.

• Excellent Python knowledge and confidence working with libraries such as Scikit-learn, Pandas, PyTorch, and SHAP.

   

Python proficiency is particularly important for this role. Our projects require Data Scientists who can independently write, structure, and maintain Data Science functionality and supporting libraries.

You should also have:

• An understanding of statistics and data analysis.
• Experience selecting and tuning loss functions such as MSE, MAE, Cross-Entropy Loss, as well as custom losses suitable for forecasting problems.
• Experience with hyperparameter optimisation tools such as Optuna or Hyperopt.
• Ability to implement and optimise PyTorch training loops, including data loaders and training/validation stages.
• Knowledge of cross-validation approaches designed specifically for time-series data.
• Understanding of regularisation techniques used to reduce overfitting.
• Strong knowledge of model evaluation metrics, including MSE, RMSE, MAE, MAPE, and R-squared.
• Familiarity with backtesting approaches used to validate model performance.
• Experience with model deployment, including model serialisation and deployment for real-time services.
• Experience preparing visualisations with matplotlib, seaborn, or Plotly.
• Confident use of Git for version control and team collaboration.
• Strong self-management and organisational skills.

What We Offer:

• Competitive remuneration package.
• Bonuses
• Professional mentorship and guidance from experienced team members.
• Opportunities for professional growth and continuous learning.
• A dynamic and collaborative work environment.

Automat-it is an all-in AWS Premier Partner and Managed Services Provider specializing in the startup ecosystem. With over 800 customers and 500+ AWS certifications, Automat-it brings hands-on expertise in AI, DevOps, and FinOps to empower fast-paced startups to grow, deliver & win. Our customers save significant time-to-market and optimize their cloud performance and costs.

We work across EMEA and the US, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech.

We’re looking for a Senior Data Scientist to join our Data Science practice and work directly with customers on complex AI and Machine Learning projects on AWS.

This is not a traditional analytics or research-focused Data Science role. You’ll work across classical ML, fine-tuning, GenAI, multimodal systems, and production ML on AWS, helping customers decide which technical approach actually fits their problem.

You’ll own projects end-to-end: understand the customer’s domain and data, evaluate different solution paths, define the architecture, implement the critical parts, and guide delivery through production.

📍 Work location: remote from Ukraine

If you are interested in this opportunity, please submit your CV in English.

Key Responsibilities

• Own Data Science projects end-to-end: Take customer problems from technical discovery through data analysis, solution design, experimentation, implementation, deployment, and production validation.
• Choose the right technical approach: Evaluate whether a problem should be solved with prompt engineering, RAG, GenAI, agentic workflows, fine-tuning, smaller language models, classical ML, Computer Vision, recommendation systems, or custom model training.
• Work deeply with data: Analyze and prepare customer datasets, identify data quality issues, create representative validation and golden datasets, and ensure the available data can support the chosen modelling approach.
• Build and fine-tune ML models: Train, fine-tune, optimize, evaluate, and deploy models using Python, PyTorch/TensorFlow, SageMaker, and modern ML tooling.
• Work with Generative AI: Build and evaluate GenAI solutions using Amazon Bedrock, RAG, prompt engineering, model selection, agentic workflows, and related AWS-native AI services.
• Use SageMaker in production: Work with SageMaker Studio, training jobs, endpoints, pipelines, model registry, batch inference, monitoring, and other production ML capabilities.
• Design production-ready solutions: Make architecture decisions across quality, latency, cost, scalability, maintainability, observability, and operational complexity.
• Work directly with customers: Participate in technical discovery, workshops, architecture discussions, and delivery conversations with founders, CTOs, engineering teams, and technical stakeholders.
• Challenge technical assumptions: Help customers avoid unnecessary complexity, explain trade-offs, costs and recommend simpler or more effective approaches when appropriate.
• Lead through technical ownership: Independently own projects with minimal supervision and mentor less experienced Data Scientists and engineers when needed.

• Collaborate across teams: Work closely with AI Engineers, MLOps, Data Engineering, DevOps, and Solution Architecture teams on customer solutions that cross multiple technical domains.

   

Requirements

• 5+ years of experience in Data Science, Machine Learning, Applied Science, or a closely related role.
• Strong classical Machine Learning fundamentals and hands-on experience building production ML solutions.
• Deep practical experience working with data, including data preparation, validation, feature engineering, dataset construction, and model evaluation.
• Strong Python skills and hands-on experience with PyTorch and/or TensorFlow.
• Strong practical experience with AWS SageMaker beyond notebook-level usage, including model training, deployment, inference, pipelines, or production operations.
• Hands-on experience with Amazon Bedrock and modern Generative AI approaches.
• Practical experience with fine-tuning models and understanding when fine-tuning is preferable to prompting, RAG, or other approaches.
• Experience in at least one strong ML domain such as NLP, Computer Vision, recommendation systems, forecasting, structured ML, multimodal ML, or similar.
• Understanding of RAG, embeddings, prompt engineering, foundation models, and agentic workflows.
• Strong understanding of MLOps and production ML practices, including model deployment, monitoring, reproducibility, lifecycle management, and CI/CD.
• Experience designing and owning solutions independently rather than working only from predefined technical specifications.
• Strong customer-facing communication skills and ability to explain technical trade-offs clearly.
• Ability to work with ambiguity, messy real-world data, and changing customer requirements.

• Strong technical judgment and a pragmatic approach to balancing model quality with delivery speed, cost, and business value.

   

Nice to have

• Experience with AgentCore, Bedrock Agents, LangGraph, Strands Agents, or other agentic frameworks.
• Experience with Small Language Models or domain-specific model adaptation.
• Experience with recommendation systems, audio ML, signal processing, or multimodal systems.
• Experience in technical consulting, pre-sales, or customer discovery.
• AWS Machine Learning certifications.
• Master’s degree or PhD in Computer Science, Machine Learning, Data Science, Mathematics, Statistics, or a related field.

What Success Looks Like in Year One

• Independently owns assigned data projects end-to-end, from understanding the problem and making technical decisions to implementation, troubleshooting, testing and successful delivery, with minimal supervision.
• Has built strong practical expertise across the team’s core data stack and can confidently work across different customer environments, including Spark, Python/SQL, data lakes/lakehouses, analytical and NoSQL databases, streaming and other technologies used by the team.
• Can investigate unfamiliar data problems independently, identify performance or data-modeling issues, understand upstream/downstream dependencies, and recommend pragmatic solutions to customers.
• Continuously expands technical depth, keeps up with modern Data technologies and patterns, and can quickly learn new technologies as customer projects and the Data practice evolve.
   

Automat-it is committed to fostering a workplace that promotes equal opportunities for all. We firmly believe that cultivating a diverse workforce is crucial to our success. Our recruitment decisions are grounded in your experience and skills, recognizing the value you bring to our team.

If you’re a talented Data Scientist looking to be part of a fast-growing, innovative R&D team, shape the future of decision-making in business, work on cuttingedge technologies and vast amounts of global real-time data, affect billions of mobile users, and become an industry leader - then let’s talk! Start.io is a mobile data platform that enables organizations to uncover insights and make datadriven decisions that enhance strategies and drive growth. We are seeking a Senior Data Scientist to join our Data Science & Engineering team.

Responsibilities:

• Analyze large and complex data to discover hidden patterns and opportunities;
• Develop and own end-to-end highly scalable data pipelines;
• Develop state-of-the-art machine learning models, and algorithms and build solutions in monetization fields;
• Provisioning data models for real-time (online) processing;
• Shape new data products, and constantly improve the existing products;
• Collaborate with multiple teams including Product, BI, Analysts, DevOps, R&D and Marketing to lead and accomplish overall solutions;
• Oversee a complete release to production, including analyzing requirements, testing, monitoring and measuring results, multitasking, and quickly responding to critical issues.

Requirements:

• B.Sc./M.A/M.Sc. degree in Computer Science, Engineering, Math, Statistics, or other equivalent fields;
• 6 years as a Data Scientist - a strong background in ML concepts and models;
• Hands-on experience with Python, especially with data science ML libraries (regression & classification algorithms e.g. XGB or LGBM, recommendation system algorithms, etc.;
• Understanding of databases and SQL for data retrieval;
• Monitoring and alerting tools (e.g. Grafana, Kibana).

Will be a plus:

• Knowledge in the programmatic advertising landscape – DSPs, SSPs, exchanges – significant advantage;
• Experience with relational (e.g. Vertica, VoltDB) and non-relational (e.g. MongoDB) databases;
• Experience with PMML.

What we offer:

• Help and support from our caring HR team;
• 20 days of vacation;
• The exchange of experience and work with talented colleagues;
• Last but not least - valuable compensation for your efforts.

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 5+ years of data science experience with direct commercial application to vulnerability management, operating with full autonomy.

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

• 5+ 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 such as TensorFlow and PyTorch;
• Experience with data visualization tools;
• Commercial experience applying data science to vulnerability management;
• 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!

Join Sigma Software to help build advanced machine learning solutions for one of the large-scale players in the programmatic advertising ecosystem. We are looking for a Senior Machine Learning Engineer with strong production ML expertise and deep interest in real-time optimization systems, large-scale behavioral data, and AdTech challenges.

In this role, you will work with a dedicated Sigma Software team on a predictive modeling platform integrated with a live ad exchange processing hundreds of millions of auction requests daily. You will contribute to sophisticated ML solutions involving bid optimization, calibration, counterfactual evaluation, and constrained decision-making systems.

We as a company offer the opportunity to work on technically challenging products, collaborate with experienced engineers and data scientists, and make a direct impact on large-scale production systems.

CUSTOMER

Our Customer is a technology company operating supply-side infrastructure within the programmatic advertising ecosystem. The company manages a high-scale ad exchange platform and is investing in predictive decisioning capabilities to improve advertising performance, audience targeting, and campaign optimization through advanced machine learning technologies.

PROJECT

The project focuses on building a predictive modeling and optimization platform on top of a live ad exchange environment. The platform evaluates and filters advertising supply in real time, predicts high-performing audience contexts, builds look-alike audiences from small seed datasets, and optimizes campaign performance across multiple business objectives and operational constraints.

The team works on complex machine learning challenges including censored bid-landscape modeling, sparse and delayed conversion attribution, calibration systems, counterfactual evaluation, and constrained optimization models. The solution is designed for large-scale production use and close collaboration with the Customer’s internal data science organization.

Job Description

• Build and improve censored bid-landscape models to estimate clearing-price distributions from partially observed auction data
•  Develop real-time win probability estimation models responsive to bid pricing dynamics
• Design and implement hierarchical lift estimation models with confidence-bound-based selection strategies
• Build conversion propensity models using sparse, delayed, and aggregate-only labels
• Develop look-alike audience modeling approaches using positive-unlabeled learning and embedding-based nearest-neighbor techniques
• Implement advertiser-level calibration strategies while independently monitoring ranking and calibration quality
• Design robust offline evaluation frameworks using inverse-propensity scoring, doubly-robust estimators, and importance reweighting
• Define exploration strategies and propensity logging approaches to ensure reliable downstream correction and evaluation
• Develop constrained optimization mechanisms for campaign objectives, pricing constraints, and volume targeting
• Contribute to data diagnostics, capability assessments, and evidence-based model recommendations
• Collaborate with the Customer team during post-launch tuning and performance validation cycles
• Prepare technical documentation and knowledge transfer materials for the Customer’s internal data science team
• Participate in architecture discussions and contribute to scalable ML platform design decisions

Qualifications

• 5+ years of experience in Machine Learning or Data Science with production-grade models measured against business KPIs
• Strong Python skills including numpy, pandas, and scikit-learn
• Strong SQL skills and experience working with large-scale datasets
• Deep practical experience with XGBoost, LightGBM, or CatBoost
• Strong understanding of regularization, calibration methods, and categorical feature handling
• Strong knowledge of probability, statistics, confidence intervals, and statistical power analysis
• Experience with feature engineering for structured and behavioral datasets
• Hands-on experience with Spark or PySpark
• Practical knowledge of experimentation frameworks and A/B testing methodologies
• Experience with advanced validation approaches including temporal splits, leakage detection, drift analysis, and slice-based metrics
• Understanding of explainability techniques such as SHAP and permutation importance
• Upper-Intermediate English level or higher

WILL BE A PLUS

• Experience in AdTech modeling including CTR/CVR prediction, bid-landscape modeling, audience segmentation, and RTB mechanics
• Experience working with sparse, delayed, or censored labels
• Knowledge of attribution modeling, survival analysis, and positive-unlabeled learning
• Practical experience with counterfactual and off-policy evaluation techniques
• Understanding of calibration methods including isotonic regression and Platt scaling
• Experience with hierarchical, empirical-Bayes, or partial-pooling models
• Knowledge of constrained or multi-objective optimization approaches
• Experience with uplift modeling and causal inference methods
• Experience with Vertex AI or similar managed ML training environments
• Publications, competitive modeling achievements, or open-source contributions related to Machine Learning or AdTech

PERSONAL PROFILE

• Strong analytical and problem-solving skills
• Ability to work effectively in a highly data-driven environment
• Strong communication and stakeholder management abilities
• Ability to explain complex modeling decisions to technical and non-technical audiences
• Proactive mindset with strong ownership mentality
• Attention to detail and scientific rigor in experimentation and evaluation

About the client

Our client is a leading European online car marketplace, connecting car buyers and sellers across the continent. The platform serves over 30 million monthly users and operates across 18 countries, making it one of the largest and most trusted digital marketplaces for vehicles in Europe.

About the role

As a Senior Data Scientist, you will be a key contributor within the client's global Data Science organization, shaping the AI and machine learning systems that sit at the core of marketplace platforms across Europe and Canada. You will design, build, and operate machine learning and GenAI systems that run in production and matter to the business — working on complex, ambiguous problems, exercising strong technical judgment, and taking responsibility for outcomes, not just models.

While this is a hands-on role, you will also influence technical direction, raise the bar for engineering practices, and enable other data scientists and engineers to succeed.

Responsibilities:

• Own ML and GenAI systems end-to-end in production, including problem framing, system design, deployment, monitoring, and continuous improvement.
• Make sound architectural and methodological decisions for ML systems on AWS, balancing robustness, observability, cost, and long-term maintainability.
• Partner closely with Product, Engineering, and Business leaders to turn ambiguous problems into ML-enabled product solutions with clear outcomes.
• Operate and evolve ML systems deployed across multiple countries and markets, accounting for differences in data distributions, regulations, and constraints.
• Mentor other data scientists through hands-on technical guidance, design reviews, and shared ownership of systems.
• Raise the bar for engineering practices across the Data Science organization and influence technical direction beyond your own projects.

Requirements:

• 5+ years of experience building and owning production ML systems end-to-end in a real-world environment.
• Advanced degree in Computer Science, Engineering, Mathematics, or a comparable field.
• Strong hands-on expertise in Python and modern ML frameworks, with the ability to ship, operate, and evolve ML systems in production.
• Solid experience with AWS and MLOps practices, including deployment, monitoring, and operating ML systems at scale.
• Practical experience applying GenAI / LLMs in applied or production contexts, with a clear understanding of trade-offs and limitations.
• Ability to influence technical decisions through clear judgment and collaboration, not formal authority.
• Experience supporting and guiding more junior data scientists through hands-on collaboration, code reviews, and technical feedback.
• Strong sense of ownership and accountability for outcomes, with a pragmatic approach to trade-offs and delivery.
• Excellent verbal and written English skills.

About the Role

Trialt is expanding its Statistical Programming team to support international Phase I-IV clinical trials across a range of therapeutic areas. Our programmers work on projects for pharmaceutical, biotechnology, medical device and CRO clients across the US and Europe. Depending on the project and individual experience, you may join a client-dedicated assignment or work within a full-service biometrics team. Project assignments are based on your experience, expertise and current business needs.

What You Will Do

• Lead statistical programming activities across one or more clinical studies.

• Oversee the development and validation of SDTM, ADaM and TLF deliverables.

• Plan programming work, estimate timelines and coordinate task allocation.

• Review complex deliverables and ensure compliance with project, CDISC and regulatory requirements.

• Maintain traceability from source data through SDTM and ADaM to final statistical outputs.

• Support the preparation and review of Define-XML, aCRF, SDRG, ADRG and validation reports.

• Investigate and resolve complex programming, data and submission-readiness issues.

• Lead client-facing meetings and technical discussions.

• Monitor project quality, timelines, risks and programming status.

• Mentor programmers and provide technical leadership.

• Collaborate closely with biostatistics, data management, medical writing and project management teams.

Required Qualifications

• Extensive statistical programming experience within clinical research.

• Advanced SAS programming skills and strong knowledge of CDISC SDTM and ADaM standards.

• Extensive experience producing and independently reviewing SDTM, ADaM and TLF deliverables.

• Experience leading programming activities at study or project level.

• Strong understanding of regulatory submission requirements and end-to-end traceability.

• Experience reviewing submission documentation and resolving Pinnacle 21 findings.

• Ability to plan work, estimate timelines, oversee quality and manage programming risks.

• Strong client-facing communication and technical leadership skills.

• Ability to mentor programmers and make independent technical decisions.

• English at Upper-Intermediate level or higher.

About the Role

Trialt is expanding its Statistical Programming team to support international Phase I-IV clinical trials across a range of therapeutic areas. Our programmers work on projects for pharmaceutical, biotechnology, medical device and CRO clients across the US and Europe. Depending on the project and individual experience, you may join a client-dedicated assignment or work within a full-service biometrics team. Project assignments are based on your experience, expertise and current business needs.

What You Will Do

• Independently develop and validate SDTM and ADaM datasets.

• Produce and QC complex tables, listings and figures.

• Interpret protocols, SAPs, dataset specifications and mock shells.

• Take ownership of programming activities for an assigned study or workstream.

• Review deliverables produced by other programmers and provide technical guidance.

• Investigate data issues, SAS log findings and validation findings.

• Support submission deliverables and study documentation.

• Estimate tasks, monitor timelines and communicate project status.

• Participate in client meetings and technical discussions.

• Collaborate with biostatisticians, data managers, medical writers and project teams.

Required Qualifications

• Solid statistical programming experience within clinical research.

• Strong SAS programming skills, including DATA Step, PROC SQL and SAS Macro Language.

• Hands-on experience with SDTM, ADaM and TLF production and QC.

• Strong working knowledge of CDISC standards and controlled terminology.

• Understanding of traceability from source data through SDTM and ADaM to statistical outputs.

• Ability to work independently with protocols, SAPs, specifications and study documentation.

• Ability to manage programming activities for an assigned study or workstream.

• Strong communication, organisation and problem-solving skills.

• English at Upper-Intermediate level or higher.

Strong Pluses

• Previous experience as a Lead Programmer or readiness to take programming ownership.

• Experience with Define-XML, aCRF, SDRG and ADRG.

• Experience using Pinnacle 21 or similar validation tools.

• Experience supporting regulatory submissions.

• Experience with R or Python.

• Previous experience in an international CRO, FSP or sponsor environment.

Working Model

• Fully remote position.

• Full-time engagement.

• Long-term cooperation.