Jobs Data Science

BigD are seeking a proactive and motivated Machine Learning Engineer to join our vibrant team. As an ML Engineer, you will play a crucial role in designing, developing, and deploying scalable machine learning models, optimizing model performance, and bridging the gap between data science research and production environments. We are interested in a full-time position.

We invite those who are fired up to

— Design, train, and deploy ML models into production environments

— Optimize model performance, scalability, and latency for high-load services

— Develop and maintain ML pipelines (data ingestion, feature engineering, training, and evaluation)

— Implement MLOps practices to streamline model deployment, monitoring, and retraining

— Collaborate with Data Engineers to ensure high-quality data pipelines and feature stores

— Explore new ML techniques and frameworks to solve complex business problems

— Maintain comprehensive documentation for models, experiments, and production infrastructure

Essential professional experience

— 2+ years of professional experience in an ML Engineer or Data Scientist role with a focus on production systems

— Proficiency in Python and deep understanding of core ML libraries (e.g., Scikit-learn, Pandas, NumPy)

— Hands-on experience with deep learning frameworks (e.g., PyTorch or TensorFlow)

— Experience with production deployment of ML models (FastAPI, Flask, or similar)

— Strong experience with MLOps tools and workflow orchestration (e.g., MLflow, Airflow, Kubeflow)

— Solid knowledge of SQL and experience working with relational databases and data warehouses (PostgreSQL, AWS, etc.)

— Proficiency in containerization and cloud orchestration (Docker, Kubernetes, AWS/GCP/Azure)

— Strong foundation in statistics, probability, linear algebra, and machine learning theory

We also appreciate

— Self-motivated with a strong ownership mentality and ability to deliver results independently

— Excellent problem-solving skills and attention to detail

— Ability to translate business requirements into technical ML solutions

— Strong communication skills for cross-functional collaboration with product and engineering teams

Working conditions:

• Direct communication with the core TEAM
• 28 calendar days of vacation
• Paid sick leave
• Sports compensation
• Compensation for courses and training
• Day off for birthday
• Flexible work schedule
• Regular salary reviews
• Salary paid at a favorable rate
• Non-toxic work environment, free of bureaucracy

• Stable salary payment

   

Join a fast-growing team at the forefront of the iGaming industry, where your expertise will directly contribute to the company's growth and success.

Domain: iGaming / Gambling
Format: Product company
Experience: 5+ years

About the Company

We are an international product company operating in the gambling sector. Our platform delivers real-time personalization for casino and sportsbook products using advanced machine learning. The solution processes live behavioral, transactional, and contextual data to improve player engagement, retention, and overall performance for operators worldwide.

Our focus is on building production-grade ML systems that directly influence what users see in real time — from game recommendations to personalized content and promotions.

Role Overview

We are looking for a Senior Data Scientist to join a product-focused team working on real-time personalization and recommendation systems for iGaming platforms.

This is a hands-on role that combines modeling, experimentation, and close collaboration with engineering and product teams in a high-load, real-time production environment.

Main Responsibilities

• Develop ML-driven features for casino products using supervised learning (regression, ranking, classification)
• Maintain and improve existing recommendation systems in production
• Enhance models using gradient boosting and other supervised approaches
• Perform data cleaning, preprocessing, and feature engineering
• Design and maintain pre- and post-processing workflows
• Optimize training and inference pipelines for performance and reliability
• Integrate ML models into Airflow pipelines in a multi-tenant environment
• Adapt and configure the solution for different clients (tenants)
• Collaborate closely with product and engineering teams on experimentation and feature delivery

As Part of the Team You Will

• Work cross-functionally with data scientists, engineers, product owners, designers, and researchers
• Analyze large-scale datasets to extract insights for product and business decisions
• Propose, implement, and evaluate ML approaches to solve real business problems
• Support and evolve a recommendation solution used across multiple tenants
• Influence product strategy through research, experimentation, and data-driven insights into user behavior

Experience & Education

• 5+ years of professional experience in data science
• Degree in a quantitative field (Mathematics, Statistics, Computer Science, or similar)

Core Skills

• Strong proficiency in Python and SQL
• Hands-on experience with data processing tools (Pandas, Polars)
• Solid engineering skills for building and maintaining scalable ML systems
• Experience implementing observability in ML pipelines (metrics, logging, alerting)
• Knowledge of Docker and Kubernetes
• Strong analytical mindset with the ability to solve loosely defined problems
• Hands-on experience with supervised ML techniques, including:
  • Regression and ranking models (XGBoost, LightGBM, CatBoost, neural networks)
  • Feature engineering and model evaluation (AUC, NDCG, MSE, uplift metrics)
  • Personalization or recommendation systems
• Proven experience deploying ML models to production (near real-time or batch)
• Solid understanding of statistical methods (A/B testing, significance testing)

Nice to Have

• Production experience with large-scale recommendation systems
• Experience with Airflow, Valkey/Redis, FastAPI in production
• Familiarity with contextual bandits or reinforcement learning
• Experience with AutoML tools

Description

As the Data Architect, you will be the senior technical visionary for the Data Platform. You will be responsible for the high-level design of the entire solution, ensuring it is scalable, secure, and aligned with the company’s long-term strategic goals. Your decisions will form the technical foundation upon which the entire platform is built, from initial batch processing to future real-time streaming capabilities.

Requirements

Required Skills (Must-Haves)

– Cloud Architecture: Extensive experience designing and implementing large-scale data platforms on Microsoft Azure.
– Expert Technical Knowledge: Deep, expert-level understanding of the Azure data stack, including ADF, Databricks, ADLS, Synapse, and Purview.
– Data Concepts: Mastery of data warehousing, data modeling (star schemas), data lakes, and both batch and streaming architectural patterns.
– Strategic Thinking: Ability to align technical solutions with long-term business strategy.

Nice-to-Have Skills:

– Hands-on Coding Ability: Proficiency in Python/PySpark, allowing for the creation of architectural proofs-of-concept.
– DevOps & IaC Acumen: Deep understanding of CI/CD for data platforms and experience with Infrastructure as Code (Bicep/Terraform)/Experience with AzureDevOps for BigData services
– Azure Cost Management: Experience with FinOps and optimizing the cost of Azure data services.

Job responsibilities

– End-to-End Architecture Design: Design and document the complete, end-to-end data architecture, encompassing data ingestion, processing, storage, and analytics serving layers.
– Technology Selection & Strategy: Make strategic decisions on the use of Azure services (ADF, Databricks, Synapse, Event Hubs) to meet both immediate MVP needs and future scalability requirements.
– Define Standards & Best Practices: Establish data modeling standards, development best practices, and governance policies for the engineering team to follow.
– Technical Leadership: Provide expert technical guidance and mentorship to the data engineers and BI developers, helping them solve the most complex technical challenges.
– Stakeholder Communication: Clearly articulate the architectural vision, benefits, and trade-offs to technical teams, project managers, and senior business leaders.

We are seeking an experienced Data Scientist with a passion for large language models (LLMs) and cutting-edge AI research. In this role, you will design and prototype data preparation pipelines, collaborating closely with data engineers to transform your prototypes into scalable production pipelines, design and implement a state-of-the-art evaluation and benchmarking framework to measure and guide model quality, and do end-to-end LLMs training. You will work alongside top AI researchers and engineers, ensuring our models are not only powerful but also aligned with user needs, cultural context, and ethical standards.

What you will do

• Curation of datasets for pre-training, supervised fine-tuning, and alignment;
• Research and develop best practices and novel techniques in LLM training and evaluation pipelines;
• Collaborate closely with data engineers, annotators, linguists, and domain experts to scale data processes, define evaluation tasks and collect high-quality feedback.

Qualifications and experience needed
Education & Experience:

• 3+ years of experience in Data Science or Machine Learning, preferably with a focus on NLP;
• An advanced degree (Master’s or PhD) in Computer Science, Computational Linguistics, Machine Learning, or a related field is highly preferred.

GenAI & NLP Expertise:

• Practical experience with fine-tuning LLMs / VLMs models;
• Hands-on experience with modern NLP approaches, including embedding models, semantic search, text classification, sequence tagging (NER), transformers/LLMs, RAGs.

ML & Programming Skills:

• Strong experience with deep learning frameworks such as PyTorch or JAX for building models;
• Ability to write efficient, clean code and debug complex model issues.

A plus would be
Advanced NLP/ML Techniques:

• Applied experience using Reinforcement Learning in NLP / LLM settings;
• Prior work on LLM safety, fairness, and bias mitigation;
• Experience generating and curating synthetic datasets for Supervised Fine-Tuning (SFT), including quality control and scaling considerations.

Research & Community:

• Publications in NLP/ML conferences or contributions to open-source NLP projects;
• Active participation in the AI community or demonstrated continuous learning (e.g., Kaggle competitions, research collaborations) indicates a passion for staying at the forefront of the field.

MLOps & Infrastructure:

• Hands-on experience with containerization (Docker) and orchestration (Kubernetes) for ML, as well as ML workflow tools (MLflow, Airflow);
• Experience in working alongside MLOps engineers to streamline the deployment and monitoring of NLP models;
• Experience with cloud platforms (AWS, GCP, or Azure) and big data technologies (Spark, Hadoop, Ray, Dask) for scaling data processing or model training is a plus.

Problem-Solving:

• Innovative mindset with the ability to approach open-ended AI problems creatively;
• Comfort in a fast-paced R&D environment where you can adapt to new challenges, propose solutions, and drive them to implementation

What we offer

• Office or remote — it’s up to you. You can work from anywhere, and we will arrange your workplace;
• Remote onboarding;
• Performance bonuses;
• We train employees with the opportunity to learn through the company’s library, internal resources, and programs from partners;
• Health and life insurance;
• Wellbeing program and corporate psychologist;
• Reimbursement of expenses for Kyivstar mobile communication.

Our Customer:

Our customer is a global, data-driven digital advertising technology company operating at large scale across mobile apps and web platforms.

Your Tasks:

• Analyze large, complex datasets to uncover patterns, insights, and business opportunities;
• Design, build, and own scalable, end-to-end data pipelines;
• Develop and productionize advanced machine learning models and algorithms, particularly in monetization-related domains;
• Build and maintain data models for real-time (online) processing;
• Contribute to the creation of new data-driven products and continuously improve existing ones;
• Collaborate with Product, BI, Analytics, DevOps, R&D, and Marketing teams to deliver complete solutions;
• Lead full production releases, including requirements analysis, testing, monitoring, result evaluation, and rapid response to critical issues.

Required Experience and Skills:

• 6+ years of experience as a Data Scientist with a strong foundation in machine learning concepts and models;
• Previous experience in the Ad-Tech industry;
• Hands-on experience working on large-scale, high-load systems processing billions of requests per second;
• Hands-on experience with Python including writing production-ready code and working with modern Python frameworks and tooling;
• Experience with data science / ML libraries and models, such as regression and classification models (e.g. XGBoost, LightGBM), recommendation systems, and related ML techniques;
• Solid understanding of databases and SQL for data analysis and retrieval;
• Experience with monitoring and alerting tools such as Grafana and Kibana;
• Proven experience working in real-time / online environments;
• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field;
• Strong end-to-end ownership mindset: ability to take solutions from research and experimentation through implementation and production;
• Fluent English with strong communication and collaboration skills.

Would Be a Plus:

• Familiarity with the programmatic advertising ecosystem (DSPs, SSPs, ad exchanges);
• Experience with relational databases (e.g. Vertica, VoltDB) and non-relational databases (e.g. MongoDB);
• Experience working with PMML.

Data Scientist - Senior - Computer vision
 

About Cyberette
Cyberette is an AI-native cybersecurity company developing forensic-grade technology to detect digital manipulation across video, audio, and images. Our mission is to restore trust in digital communication and protect people, organizations, and institutions from deception in the AI era.

About the Role
We are looking for a Computer Vision Data Scientist with strong initiative and technical excellence to join our core development team. You’ll be responsible for designing, training, and optimizing computer vision models and architectures used in our deepfake detection and forensic analysis platform.

This role requires someone who thrives in fast-paced environments, loves solving complex problems, and brings both creativity and rigor to their work. You’ll collaborate closely with our founders and engineers to push the limits of multimodal AI for real-world impact.

Key Responsibilities

• Develop, train, and deploy computer vision models for image and video manipulation detection.
• Research and experiment with new architectures, model ensembles, and multimodal fusion techniques.
• Preprocess, clean, and manage large-scale datasets for training and evaluation.
• Optimize model accuracy, latency, and scalability for enterprise-grade performance.
• Contribute to the design of the model evaluation framework and internal benchmarking tools.
• Collaborate with other engineers and researchers to integrate models into production pipelines.
• Document methodologies, experiments, and findings clearly and systematically.

Requirements

• MSc/PhD in Computer Science, Machine Learning, Artificial Intelligence, or related field.
• Strong experience in computer vision (CNNs, Transformers, multimodal models).
• Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow).
• Solid understanding of ML ops, dataset curation, and model evaluation.
• Proven experience building and training models from scratch.
• Excellent analytical and problem-solving skills.
• Self-driven, disciplined, and collaborative mindset.

What We Value
At Cyberette, we believe technology should serve people. We are a team of builders united by a shared sense of purpose — creating tools that make the digital world safer and more truthful. If you care deeply about impactful innovation and want your work to matter, you’ll feel at home here.

Bonus Skills

• Experience with audio or multimodal analysis.
• Knowledge of adversarial attacks or deepfake generation methods.
• Experience in real-time inference or edge deployment.

We are looking for a Marketing / Commercial Data Scientist to build the intelligence layer of our Session Analyzer product — including scoring systems, segmentation, recommendation models, and causal measurement frameworks for pharmaceutical marketing teams.

This role sits at the intersection of behavioral data, machine learning, and marketing analytics. You will work end-to-end: from raw event data → feature engineering → modeling → production pipelines → business insights.
 

You’ll collaborate closely with Product and Tech Leads to turn complex behavioral signals into actionable commercial intelligence.

🛠 Requirements

• 5+ years in Data Science / Analytics
• Experience building production ML systems
• Strong ownership of end-to-end data science workflows
   

Technical Skills

• Python, SQL, PostgreSQL
• GCP / BigQuery /ClickHouse
• Data pipelines & feature engineering
• Experience with LLM-based agents / AI workflows
• Event-level / session analytics
• Aggregation modeling
• Propensity / engagement modeling
• Segmentation
• Recommendation systems
• Model evaluation & monitoring
• Experience in AdTech / MarTech / Marketing Analytics
• Marketing attribution & measurement
• Behavioral / clickstream / pixel data
   

🧠 What You’ll Work On

🔹 Behavioral & Scoring Models

• Build propensity and engagement models (e.g., prescribing likelihood, rep response, content affinity)
• Develop switch / churn-style models and brand adoption predictions
• Design segmentation frameworks based on behavioral and profile data
   

🔹 Recommendation & Personalization

• Build recommendation systems using session and event-level data
• Model multi-channel engagement and content affinity
   

🔹 Causal Inference & Attribution

• Design and implement marketing attribution models
• Apply causal inference techniques (uplift modeling, incrementality testing, treatment/control analysis)
• Connect marketing exposure → downstream outcomes
   

🔹 Data & Feature Engineering

• Build feature pipelines from event/session data (clicks, scroll depth, dwell time, downloads, video plays)
• Create aggregated feature tables and scoring datasets
• Work with high-volume data systems (e.g., ClickHouse, BigQuery)
   

🔹 Production & Data Quality

• Deploy and maintain production ML pipelines
• Implement data validation, monitoring, anomaly detection, and drift tracking
   

🔹 AI & Insight Automation

• Build LLM-powered workflows for:
  • Automated insight generation
  • Data diagnostics
  • Structured analytical outputs (e.g., JSON)
     

🎯 What Success Looks Like

• You build reliable scoring systems used in production
• Your models drive measurable marketing impact
• You translate behavioral data into clear, actionable insights
• You help evolve the system toward real-time intelligence & automation
   

🧪 Interview Process

• Screening Interview
• Technical Interview
• Bar Raiser

We are looking for a Senior Data Scientist to build the core intelligence layer of our Session Analyzer product: scoring, audience segmentation, and prescriptive recommendations for pharmaceutical marketing teams. You will operate as a “full-stack” data scientist across data validation, data engineering (prod pipelines), ML modeling, causal measurement, and AI-assisted workflows (agents for insights and data diagnostics). You will work directly with the Product Lead and Tech Lead.

Responsibilities

- Design and ship scoring, segmentation, and recommendation features based on pixel session/event data, NPI-enriched profiles, and 3rd-party datasets.

- Build a data quality & validation framework from scratch: tests, monitors, anomaly detection, drift tracking, and root-cause workflows across the data chain.

- Deliver batch PoC (days latency) and evolve to near-real-time MVP (hours latency) pipelines and feature tables.

- Develop and evaluate ML models for engagement prediction and audience targeting; ensure calibration, stability, and business-relevant lift.

- Define and implement measurement/attribution approaches to support ROI uplift and explain “what worked / why”.

- Build AI agents for (1) automated data issue diagnostics and (2) insight/presentation generation, with guardrails and traceability.

- Produce outputs as score tables, top insights, aggregates/features, dashboards/reports, and APIs for audience activation and CDP/CRM integrations.

Requirements

- 5+ years in Data Science / Analytics roles with production ownership.

- Strong Python + SQL; hands-on building production pipelines and data products.

- Experience with event/session analytics, aggregation modeling, and working with noisy web/pixel data.

- Proven ability to build propensity/engagement models, segmentation, and recommendation logic; strong evaluation practices.

- Working knowledge of causal inference / attribution methods for marketing effectiveness.

- Experience with GCP / BigQuery in production; familiarity with ClickHouse/Postgres is a plus.

- Ability to operate end-to-end: problem framing → data validation → modeling → deployment → monitoring → stakeholder comms.

- Hands-on experience building and operating data-driven marketing/advertising analytics products, including event-level measurement, audience segmentation, activation workflows, and performance attribution (AdTech/MarTech environments).

Nice-to-have: 

- Experience with agents (LLM-based workflows), automated insights, and safe deployment patterns.

- Familiarity with data quality tooling (e.g., Great Expectations/dbt tests) and observability.

What We Offer

- Fully remote with flexible hours (aligned with EU timezones for syncs).
- Influence on quality strategy across the entire engineering organization.
- A cross-team role with visibility into every part of the product.
- AI-first tooling.
- Claude Code licenses and cutting-edge AI development workflows. 
- A team with no bureaucracy, decisions are made fast.

Computer Vision engineer

DeepX is looking for a Computer Vision engineer

Core requirements:
- Python development experience
- Knowledge of popular data processing libraries (numpy, pandas, scipy etc)
- GIT, Linux, bash

Nice to have (or able to learn quickly):
- Hands-on experience with popular Machine Learning frameworks (OpenCV, Pytorch, Tensorflow, Keras, Yolo)
- Understanding of Computer Vision problem space
- Machine Learning and Artificial Neural Networks fundamentals
- Linux, Docker, gcc/make etc experience
- C++ and C, creating Python C extensions

A bit about DeepX company:
DeepX is an R&D intensive and innovation-driven consortium that provides Artificial Intelligence-powered Computer Vision solutions for businesses.
Please, take a look to find out more about us https://deepxhub.com/
We are a group of enthusiastic adherents who are passionate about the idea of improving this world’s safety by making Computer Vision technologies more advanced. Lifetime learning is our motto, we are admire ambition hard workers who are ready to face new challenges. If you feel like we might be on the same page, please, apply!

Position Overview

As a Game Mathematician, you will own the mathematical design of our gaming systems — from core probability models to long-term behavioral dynamics of the game.

You will shape RTP, volatility curves, hit frequency, payout distributions, risk balance, and overall economic sustainability of each title.

This role requires deep theoretical knowledge, practical simulation skills, and the ability to collaborate closely with producers, designers, engineers, and compliance teams.

Responsibilities

• Develop and maintain detailed mathematical models for Slots, Crash Games, and Fast Games.

• Design and balance RTP, volatility, hit frequency, paytables, game curves.

• Model stochastic processes and RNG logic, ensuring correctness and predictability.

• Calculate probabilities and payout distributions across long-run simulations.

• Conduct large-scale simulations and statistical analysis of game behavior.

• Balance risk, retention, and economic KPIs of the product.

• Collaborate with producers to create optimal player experience.

• Work closely with engineers to validate correct technical implementation.

• Contribute to game logic design and innovative mechanics.

• Conduct competitive analysis of game mathematics and market trends.

• Participate in playtests and analyze real in-game behavior post-launch.

• Prepare complete mathematical documentation for certification and regulators.

Requirements

• Strong knowledge of probability theory, combinatorics, and mathematical statistics.

• Experience in slot or gambling game mathematics (3+ years).

• Practical experience designing and balancing RTP, volatility, and payout structures.

• Experience with stochastic modeling and simulations.

• Programming skills (Python, R, C++, MATLAB, or similar).

• Ability to clearly communicate mathematical decisions to cross-functional teams.

• English — Intermediate or higher.

Would Be a Plus

• Experience with crash or fast game mechanics.

• Experience with competitive math research.

• Understanding of player behavior and retention impact.

• Experience interacting with certification bodies and regulators.

What We Offer

• Direct influence on product economics and player experience.

• Real ownership of game mathematics and long-term performance.

• Competitive compensation.

• Professional growth inside a product-driven gambling studio.

• Flexible working format.

• Strong, collaborative team environment.

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

About the role:
We are looking for a hands-on Sr. Machine Learning / Computer Vision Engineer with strong practical experience in designing, training, evaluating, and improving vision systems for real-world applications. The ideal candidate combines solid theoretical ML/CV fundamentals with an engineering mindset and is comfortable taking ownership of the full pipeline, from data preparation and experimentation to model optimization and deployment.

Responsibilities:
- Design, train, evaluate, and improve machine learning and computer vision models for real-world production use cases
- Build and maintain end-to-end ML pipelines, including data collection support, preprocessing, feature engineering, training, validation, and inference
- Develop solutions across both computer vision and machine learning tasks depending on business needs
- Perform error analysis, diagnose model weaknesses, and drive improvements in accuracy, robustness, and reliability
- Select and justify appropriate methods and model architectures based on dataset characteristics, product constraints, and deployment requirements
- Work with challenging real-world data, including noisy, limited, imbalanced, or weakly labeled datasets
- Collaborate with engineering and product teams to translate business problems into practical ML solutions
- Contribute to production-ready development with a focus on maintainability, reproducibility, and performance

Requirements:
- 3–5+ years of professional experience in Machine Learning / Computer Vision
- Strong senior-level practical expertise in computer vision, machine learning, and applied model development
- Hands-on experience with tasks such as object detection, image classification, anomaly detection, feature-based approaches, or similar
- Solid understanding of classical ML methods, including tree-based models, linear models, feature engineering, model selection, and evaluation
- Strong Python skills and practical experience with PyTorch, OpenCV, and common ML tooling
- Experience building full ML workflows including data preparation, augmentation, preprocessing, training, validation, testing, and deployment handoff
- Strong understanding of model evaluation, debugging, overfitting, generalization, optimization, and experiment-driven improvement
- Ability to write clean, modular, maintainable code and independently own technical solutions
- Strong analytical thinking, engineering maturity, and problem-solving skills

Nice to Have:
- Experience in adjacent ML domains such as time-series, audio, or NLP
- Experience with embedding-based methods, metric learning, retrieval systems, or memory-bank approaches
- Practical experience with model optimization techniques such as quantization, pruning, distillation, or related efficiency-focused approaches
- Experience working with real-world industrial datasets and ambiguous problem formulations
- Knowledge of software engineering best practices, reproducible experimentation, and pipeline design

We offer:
- Free English classes with a native speaker and external courses compensation;
- PE support by professional accountants;
- Medical insurance;
- Team-building events, conferences, meetups, and other activities;
- There are many other benefits you’ll find out at the interview.

Role Summary

We are looking for a Senior Data Scientist to lead the data intelligence layer of an AI-driven platform. This role focuses on extracting insights from structured and unstructured documents, building document processing and OCR pipelines, improving semantic retrieval systems, identifying information gaps in datasets, and maintaining high standards of data quality across end-to-end data workflows.
 

Key Responsibilities

• Design and improve OCR and document intelligence pipelines that convert complex PDFs and supporting files into structured, machine-readable data.
• Develop semantic retrieval workflows that identify relevant historical records and support recommendation systems based on similarity and contextual matching.
• Create data-driven methods to detect missing, incomplete, or inconsistent information within document-based datasets.
• Define and monitor data quality standards, validation rules, completeness checks, and anomaly detection mechanisms across data pipelines.
• Analyze structured and unstructured datasets to uncover patterns affecting operational performance, classification quality, and decision outcomes.
• Collaborate with machine learning engineers to prepare high-quality datasets for model training, validation, and iterative improvement.
• Develop labeling strategies, feature definitions, and dataset enhancement workflows for early-stage and production use cases.
• Evaluate the performance of retrieval systems, document extraction pipelines, and information coverage.
• Work closely with engineering and product teams to operationalize insights through dashboards, tools, and data-driven workflows.
• Communicate analytical findings, experiment results, and actionable insights clearly to technical and non-technical stakeholders.
   

Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Artificial Intelligence, or a related technical field.
• 5+ years of experience in data science, applied NLP, document intelligence, or analytics-focused roles. 
• Strong proficiency in Python and practical experience with data science libraries and NLP tooling. 
• Experience building document processing or OCR pipelines and working with semi-structured or noisy datasets.
• Strong understanding of embeddings, semantic search, retrieval evaluation, and similarity-based recommendation systems.
• Experience with data cleaning, validation, feature engineering, and dataset quality management.
• Ability to design experiments and translate analytical findings into product or model improvements.
• Strong SQL skills and solid analytical problem-solving abilities.
   

Nice to Have

• Experience working with AI systems in regulated or compliance-sensitive environments.
• Familiarity with multilingual NLP applications or systems that support multiple languages.
• Experience with vector databases or retrieval-augmented architectures.
• Exposure to annotation workflows and human-in-the-loop review processes.
• Experience building dashboards, defining operational KPIs, or supporting data-driven reporting.
  
  We offer:
• Flexible working schedule
• Fully remote work
• Compensation for medical, educational and sports activities
• Professional growth opportunities
• Internal knowledge-sharing talks
• Awesome team events and activities
• Paid vacation and sick leave

We are looking for a Middle/Senior Machine Learning 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.

Key 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.
• 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).

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

About the role:
We are looking for a hands-on Sr. Machine Learning / Computer Vision Engineer with strong practical experience in designing, training, evaluating, and improving vision systems for real-world applications. The ideal candidate combines solid theoretical ML/CV fundamentals with an engineering mindset and is comfortable taking ownership of the full pipeline, from data preparation and experimentation to model optimization and deployment.

Responsibilities:
- Design, train, evaluate, and improve machine learning and computer vision models for real-world production use cases
- Build and maintain end-to-end ML pipelines, including data collection support, preprocessing, feature engineering, training, validation, and inference
- Develop solutions across both computer vision and machine learning tasks depending on business needs
- Perform error analysis, diagnose model weaknesses, and drive improvements in accuracy, robustness, and reliability
- Select and justify appropriate methods and model architectures based on dataset characteristics, product constraints, and deployment requirements
- Work with challenging real-world data, including noisy, limited, imbalanced, or weakly labeled datasets
- Collaborate with engineering and product teams to translate business problems into practical ML solutions
- Contribute to production-ready development with a focus on maintainability, reproducibility, and performance

Requirements:
- 3–5+ years of professional experience in Machine Learning / Computer Vision
- Strong senior-level practical expertise in computer vision, machine learning, and applied model development
- Hands-on experience with tasks such as object detection, image classification, anomaly detection, feature-based approaches, or similar
- Solid understanding of classical ML methods, including tree-based models, linear models, feature engineering, model selection, and evaluation
- Strong Python skills and practical experience with PyTorch, OpenCV, and common ML tooling
- Experience building full ML workflows including data preparation, augmentation, preprocessing, training, validation, testing, and deployment handoff
- Strong understanding of model evaluation, debugging, overfitting, generalization, optimization, and experiment-driven improvement
- Ability to write clean, modular, maintainable code and independently own technical solutions
- Strong analytical thinking, engineering maturity, and problem-solving skills

Nice to Have:
- Experience in adjacent ML domains such as time-series, audio, or NLP
- Experience with embedding-based methods, metric learning, retrieval systems, or memory-bank approaches
- Practical experience with model optimization techniques such as quantization, pruning, distillation, or related efficiency-focused approaches
- Experience working with real-world industrial datasets and ambiguous problem formulations
- Knowledge of software engineering best practices, reproducible experimentation, and pipeline design

We offer:
- Free English classes with a native speaker and external courses compensation;
- PE support by professional accountants;
- Medical insurance;
- Team-building events, conferences, meetups, and other activities;
- There are many other benefits you’ll find out at the interview.

About Tie

Tie is building the next generation of identity resolution and marketing intelligence. Our platform connects hundreds of millions of consumers across devices, browsers, and channels—without relying on cookies—to power higher deliverability, smarter targeting, and measurable revenue lift for modern marketing teams.

At Tie, AI is not a feature—it is a core execution advantage. We operate large-scale identity graphs, real-time scoring systems, and production ML pipelines that directly impact revenue, deliverability, and customer growth.

The Role

We are looking for a Senior AI / Machine Learning Engineer to design, build, and deploy production ML systems that sit at the heart of our identity graph and scoring platform. You will work at the intersection of machine learning, graph data, and real-time systems, owning models end to end—from feature engineering and training through deployment, monitoring, and iteration.

This role is highly hands-on and impact-driven. You will help define Tie’s ML architecture, ship models that operate at sub-second latency, and partner closely with platform engineering to ensure our AI systems scale reliably.

What You’ll Do

• Design and deploy production-grade ML models for identity resolution, propensity scoring, deliverability, and personalization
• Build and maintain feature pipelines across batch and real-time systems (BigQuery, streaming events, graph-derived features)
• Develop and optimize classification models (e.g., XGBoost, logistic regression) with strong handling of class imbalance and noisy labels
• Integrate ML models directly with graph databases to support real-time inference and identity scoring
• Own model lifecycle concerns: evaluation, monitoring, drift detection, retraining, and performance reporting
• Partner with engineering to expose models via low-latency APIs and scalable services
• Contribute to GPU-accelerated and large-scale data processing efforts as we push graph computation from hours to minutes
• Help shape ML best practices, tooling, and standards across the team

What You’ll Bring

Required Qualifications

• 5+ years of experience building and deploying machine learning systems in production
• Strong proficiency in Python for ML, data processing, and model serving
• Hands-on experience with feature engineering, model training, and evaluation for real-world datasets
• Ability to travel outside of Ukraine is a must
• Experience deploying ML models via APIs or services (e.g., FastAPI, containers, Kubernetes)
• Solid understanding of data modeling, SQL, and analytical workflows
• Experience working in a cloud environment (GCP, AWS, or equivalent)
• Experience with graph data, graph databases, or graph-based ML

• Familiarity with Neo4j, Cypher, or graph algorithms (community detection, entity resolution)

  Preferred / Bonus Experience

• Experience with XGBoost, tree-based models, or similar classical ML approaches
• Exposure to real-time or streaming systems (Kafka, Pub/Sub, event-driven architectures)
• Experience with MLOps tooling and practices (CI/CD for ML, monitoring, retraining pipelines)
• GPU or large-scale data processing experience (e.g., RAPIDS, CUDA, Spark, or similar)
• Domain experience in identity resolution, marketing technology, or email deliverability

Our Technology Stack

• ML & Data: Python, Pandas, Scikit-learn, XGBoost
• Graphs: Neo4j (Enterprise, GDS)
• Cloud: Google Cloud Platform (BigQuery, Vertex AI, Cloud Run, Pub/Sub)
• Infrastructure: Docker, Kubernetes, GitHub Actions
• APIs: FastAPI, REST-based inference services

What We Offer

• Competitive compensation, including salary, equity, and performance incentives
• Opportunity to work on core AI systems that directly impact revenue and product differentiation
• High ownership and autonomy in a senior, hands-on role
• Remote-first culture with a strong engineering and data focus
• Exposure to cutting-edge problems in identity resolution, graph ML, and real-time AI systems

• Clear growth path toward Staff / Principal IC roles

  What else:

• 4 weeks of paid vacation per year (flexible scheduling)
• Unlimited sick leave — we trust your judgment and care about your health
• US Bank Holidays off (American calendar)
• Remote-first culture and flexible working hours
• Flat structure, no micromanagement, and full ownership

• Opportunity to make a real impact during a critical growth phase

  Interview Process

• Recruitment Screening Call
• Initial call with Head of Data Science & AI and CTO  (30 min) in English
• Technical deep dive Interview (1,5h)  in English

• Optional test-task (paid)

  Why Join Us?

• High-impact delivery leadership role during a critical period
• Real ownership and autonomy
• Opportunity to shape delivery across the entire engineering organization
• Exposure to SaaS, data, integrations, automation, and platform work
• Collaboration with global teams and vendors
• A strong product with real scale and momentum

Why This Role Matters

At Tie, your work will not live in notebooks or experiments—it will power production systems used by real customers at scale. You will help define how AI is embedded into the company’s core platform and play a key role in making machine learning a durable competitive advantage.