Jobs Data Science

About Us

We are a newly funded MilTech startup building an on-premises platform for automated annotation of petabytes of UAV video and training computer vision models for autonomous flight. We have a strategic partnership with the largest Ukrainian UAV manufacturer and access to what is the largest privately owned UAV video dataset in the world. We are at the very beginning — the technical plan exists, the funding is secured, the data is real — and now we need the right people to turn it into reality.

The Role

We are looking for a Data Annotator / CVAT Specialist to join the founding team. You will be the person who turns raw UAV video into the high-quality labeled datasets that our ML models are trained on. This is not bulk labeling work — you will help define annotation standards, manage the CVAT platform, build quality control workflows, and directly shape the data that drives our autonomous flight models.

You will work closely with the ML engineering team to establish the annotation pipeline from scratch: from ontology design and labeling policy through annotation execution, quality assurance, and feedback loops that continuously improve both label quality and annotation speed.

What You Will Own

CVAT Administration & Workflow
- Set up projects, tasks, and jobs; manage annotation workflows and task routing
- Configure AI-assisted annotation: integrate pre-trained models for auto-labeling and tracking
- Manage user roles, permissions, and review pipelines within CVAT
- Handle data import/export across formats (COCO, YOLO, MOT, CVAT XML)

Annotation Execution
- Annotate UAV video clips using bounding boxes, multi-object tracking (Track Mode with keyframes and interpolation), and classification labels
- Handle complex scenarios: occluded objects, crowded scenes, night/low-visibility footage, fast-moving targets
- Achieve and maintain high throughput without sacrificing label quality

Ontology & Labeling Policy
- Collaborate with the ML team to define and refine the annotation ontology (class definitions, examples, edge cases)
- Document labeling rules: occlusion/truncation handling, minimum box sizes, track start/end criteria, disappearance rules
- Maintain and update annotation guidelines as new classes or scenarios emerge

Quality Control
- Execute the QC pipeline: gold set calibration, double-labeling for inter-annotator agreement, review workflows
- Run automated validation checks: geometry (out-of-frame, zero-area, broken aspect ratios), temporal continuity (teleporting boxes, scale jumps, broken tracks)
- Track quality metrics: agreement scores (IoU + class match), rework rates, model-vs-human correction rates
- Identify systematic errors and feed them back into guideline updates and annotator calibration

Scaling & Active Learning Support
- Process pre-labeled data from ML models: review, correct, and approve auto-annotations based on confidence routing (high/mid/low)
- Prioritize annotation effort on hard cases (low confidence, rare classes, difficult conditions)
- Provide structured feedback to the ML team on where pre-labels consistently fail, driving retraining priorities

What We Are Looking For

Must Have
- 2+ years of hands-on experience in data annotation for computer vision (object detection, tracking, or segmentation)
- Strong proficiency with CVAT — project setup, Track Mode, interpolation, AI-assisted annotation, import/export
- Experience annotating video data (not just static images) — understanding of temporal consistency and tracking
- Familiarity with annotation formats: COCO, YOLO, MOT, Pascal VOC
- Attention to detail and ability to maintain high label quality at volume
- Experience with quality control processes: review workflows, inter-annotator agreement, gold sets
- Comfortable working in Linux environments and with self-hosted tools

Strong Plus
- Experience administering a self-hosted CVAT deployment (Docker-based setup, Nuclio for serverless auto-annotation)
- UAV / drone / aerial imagery annotation experience
- Familiarity with other annotation tools (Label Studio, Supervisely, V7, Roboflow)
- Understanding of ML model evaluation metrics (mAP, IoU, precision/recall) and how annotation quality affects them
- Experience with active learning or pre-annotation workflows
- Basic Python scripting (format conversion, data validation, automation)
- MilTech or defense-related annotation work

What We Offer

- Join an early-stage team where your work directly determines the quality of the data behind our ML models
- Define annotation standards and QC processes from scratch — not just follow someone else's guidelines
- The largest privately owned UAV video dataset in the world — petabyte-scale, impossible to replicate
- Work closely with ML engineers and see how your annotations translate into model performance
- Opportunity to grow into annotation team lead as the team scales
- Open-source-first approach with no vendor lock-in
- Competitive salary
 

In Competera, we are building a place where optimal pricing decisions can be made easily. We believe that AI technologies will soon drive all challenging decisions and are capable of helping humans be better. We are now looking for a Middle Data Scientist to play a key role in reshaping the way we provide our solutions.
 

You could be a perfect match for the position if

You want to:

• Validate datasets to ensure data accuracy and consistency.
• Design proof-of-concept (POC) solutions to explore new approaches.
• Develop technical solutions by mapping requirements to existing tools and functionalities.
• Train models and create custom approaches for new domains.
• Troubleshoot data processing and model performance issues.

You have:

• 2+ years of experience in data science or a related field.
• Strong SQL skills for data manipulation and extraction.
• Proficiency in Python, with the ability to write modular and readable code for experiments and prototypes.
• A solid mathematical background, preferably in a Computer Science-related field.
• Expertise in scientific Python libraries, including NumPy, pandas, scikit-learn, and either Keras/TensorFlow or PyTorch.
• Familiarity with Time Series Forecasting methodologies.
• Experience in statistical testing, including A/B testing.
• 1+ years working with tabular and multimodal data (e.g., combining tabular data with text, audio, or images).
• Upper-intermediate or higher English level.

Soft skills:

• Analytical mindset and critical thinking to solve complex problems.
• Agile approach, with the ability to experiment and test hypotheses in a dynamic business environment.
• Business-oriented thinking, capable of translating complex models into clear business insights.
• Curiosity and a drive for continuous learning in the data domain.
• Strong team player, able to collaborate across cross-functional teams.

You’re gonna love it, and here’s why:

• Rich innovative software stack, freedom to choose the best suitable technologies.
• Remote-first ideology: freedom to operate from the home office or any suitable coworking.
• Flexible working hours (we start from 8 to 11 am) and no time tracking systems on.
• Regular performance and compensation reviews.
• Recurrent 1-1s and measurable OKRs.
• In-depth onboarding with a clear success track.
• Competera covers 70% of your training/course fee.
• 20 vacation days, 15 days off, and up to one week of paid Christmas holidays.
• 20 business days of sick leave.
• Partial medical insurance coverage.
• We reimburse the cost of coworking.

Drive innovations with us. Be a Competerian.

Data Scientist – 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. 

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.

We value ownership, fast responses, and a goal-oriented mindset. Our environment is collaborative and fast-moving, and we appreciate people who communicate proactively, take initiative, and focus on delivering meaningful results.

 

Bonus Skills

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

Role Overview

Nucleus AI is seeking an AI / ML Data Scientist to help build and scale an AI-backed Learning Management System (LMS) that delivers personalized, adaptive learning experiences.

This is a hands-on, product-focused role at the intersection of machine learning, data science, and learning technology. You will design and deploy intelligent systems that directly impact learner engagement, personalization, assessment, and outcomes.

If you enjoy turning real-world data into models that are used by real users—and iterating on them in production—this role is for you.

Key Responsibilities

Machine Learning & Modeling

• Design, develop, and deploy machine learning models that power:
  • Personalized learning paths
  • Content and course recommendations
  • Learner analytics and insights
  • Adaptive assessments and feedback
     
• Build models for:
  • Learner skill inference and knowledge tracing
  • Engagement, completion, and drop-off prediction
  • Automated assessment scoring and feedback
     

Data & Analytics

• Analyze large-scale learner behavior data to extract actionable insights
• Develop and maintain data pipelines, feature engineering workflows, and model evaluation frameworks
• Apply statistical analysis and experimentation (including A/B testing) to validate model performance and impact
   

Collaboration & Product Integration

• Work closely with product managers, engineers, and instructional designers to translate learning objectives into AI-driven solutions
• Integrate models into production systems via APIs, batch pipelines, or real-time inference
   

LLMs & Advanced Techniques

• Experiment with and integrate LLMs and NLP techniques for:
  • Content generation
  • Learner feedback
  • Intelligent learner support
     
• Monitor models in production for performance, bias, and drift, and continuously improve them
   

Documentation & Governance

• Document models, assumptions, experiments, and results to ensure transparency, reproducibility, and maintainability
   

Required Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, AI/ML, Statistics, or a related field
• Strong proficiency in Python and common ML libraries (e.g., scikit-learn, PyTorch)
• Solid understanding of:
  • Supervised and unsupervised learning
  • Feature engineering and model evaluation
  • Statistical analysis and experimentation
     
• Experience working with both structured and unstructured data
• Proficiency in SQL and working with large datasets
• Ability to clearly communicate complex technical concepts to non-technical stakeholders
   

Nice-to-Have Qualifications

• Experience building ML systems in ed-tech, LMS platforms, or learning analytics
• Familiarity with
  • Large Language Models (LLMs)
  • NLP
  • Recommendation systems
  • Knowledge graphs
• Experience deploying models to production environments
• Exposure to cloud platforms such as AWS, GCP, or Azure
• Understanding of learning science, instructional design, or assessment theory
• Experience with MLOps tools (model versioning, monitoring, CI/CD for ML)
   

What We Offer

• Opportunity to work on mission-driven AI that improves how people learn
• Ownership of ML systems used by real learners at scale
• A collaborative, cross-functional team culture
• Competitive compensation and benefits
• Flexible work location and schedule
• Continuous learning and professional growth opportunities

The project's purpose is development of AI models for surgical phase recognition and anatomical structure detection during laparoscopic gallbladder removal procedures. Annotators will receive structured training and continuous feedback from medical experts. 

Requirements: 

• Strong knowledge and proven experience with Machine Learning, Computer Vision, and Deep Learning
• At least 3 years of commercial experience in a related field
• Self-manageable person, able to work with minimum supervision
• Technically strong with Python; knowledge of any other programming language will be a plus
• Proficiency with NumPy, SciPy, OpenCV, TensorFlow
• Proven ability to design and implement complex algorithms, and to work creatively and analytically in a problem-solving environment
• Bachelor's degree or equivalent in Computer Science, Engineering, Mathematics, or a related field
• Strong communication and presentation skills for effective daily communication
• Experience with SAM2 / SAM3 (Segment Anything Models) will be a plus
• Experience with medical image segmentation will be a plus
• Experience developing action recognition models will be plus

Responsibilities: 

• Design and develop machine learning and deep learning models for surgical phase recognition and anatomical structure detection in laparoscopic video data
• Implement and optimize computer vision algorithms using Python, NumPy, SciPy, OpenCV, and TensorFlow
• Build data processing and training pipelines for large-scale medical video datasets
• Collaborate with medical experts and annotation teams to ensure high-quality labeled data and continuous model improvement
• Evaluate, optimize, and deploy models to ensure reliable performance in real-world clinical environments
• Research and apply state-of-the-art computer vision and deep learning techniques to improve system accuracy and robustness

What is in for You: 

Culture of caring. At GlobalLogic, we prioritize a culture of caring. Across every region and department, at every level, we consistently put people first. From day one, you’ll experience an inclusive culture of acceptance and belonging, where you’ll have the chance to build meaningful connections with collaborative teammates, supportive managers, and compassionate leaders. 

Learning and development. We are committed to your continuous learning and development. You’ll learn and grow daily in an environment with many opportunities to try new things, sharpen your skills, and advance your career at GlobalLogic. With our Career Navigator tool as just one example, GlobalLogic offers a rich array of programs, training curricula, and hands-on opportunities to grow personally and professionally.

Interesting & meaningful work. GlobalLogic is known for engineering impact for and with clients around the world. As part of our team, you’ll have the chance to work on projects that matter. Each is a unique opportunity to engage your curiosity and creative problem-solving skills as you help clients reimagine what’s possible and bring new solutions to market. In the process, you’ll have the privilege of working on some of the most cutting-edge and impactful solutions shaping the world today.

Balance and flexibility. We believe in the importance of balance and flexibility. With many functional career areas, roles, and work arrangements, you can explore ways of achieving the perfect balance between your work and life. Your life extends beyond the office, and we always do our best to help you integrate and balance the best of work and life, having fun along the way!

High-trust organization. We are a high-trust organization where integrity is key. By joining GlobalLogic, you’re placing your trust in a safe, reliable, and ethical global company. Integrity and trust are a cornerstone of our value proposition to our employees and clients. You will find truthfulness, candor, and integrity in everything we do.

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 the role
You’ll build enterprise dashboards, reporting systems, and automation workflows, ensuring KPI standardization, reporting accuracy, and data governance compliance while improving operational efficiency through structured automation.

What you’ll do

• Build Power BI dashboards aligned to business KPIs; advanced DAX measures; standardized templates; performance optimization 
• Design data models and ETL pipelines; validate data quality; maintain dimensional modeling standards; optimize SQL performance 
• Own KPI governance: formal KPI definitions/formulas, centralized KPI dictionary, prevent conflicting metrics 
• Build Power Automate workflows (approvals/notifications/triggers) with reliability + exception handling; API-based automation integration 
• Enforce data governance/security: RBAC + Power BI row-level security; compliance with access policies 
• Validate outputs vs source systems; reconciliation checks; audit trails; RCA for discrepancies 

Must-have

• 5+ years BI/data analytics; enterprise reporting; strong relational DB background; Agile experience 
• Power BI (advanced DAX), data modeling (star/snowflake), report optimization 
• SQL Server (advanced queries, stored procedures, indexing) 
• ETL: SSIS or Azure Data Factory preferred; data warehouse concepts 
• Power Automate (cloud/desktop flows), REST/JSON integrations, orchestration logic 
• Azure data services (Azure SQL, ADF, Synapse) + governance (RBAC/RLS, privacy/compliance) 

Authority / constraints

• Owns KPI modeling and automation framework principles; cannot redefine KPIs without PO/business alignment; cannot bypass governance/access policy 

Certs

• PL-300 mandatory; DP-203 recommended

About Forecasa

Forecasa is a profitable, founder‑led SaaS company that turns raw real‑estate transaction data into decision‑grade intelligence for hedge funds, private‑lenders, and MBS desks. We move fast, value autonomy with accountability, and maintain a culture where clear documentation beats hierarchy.

What you’ll do

• Engineer risk‑focused features (borrower, lender, property, geography)  in Python/PySpark.
• Develop and validate PD / LGD models using WoE, IV, logistic GBM, XGBoost, or similar.
• Prototype lender‑health metrics (capital‑diversification, portfolio turnover, market concentration, etc.) for client dashboards.
• Create robust, reproducible data pipelines (git‑versioned, unit‑tested, CI in GitLab).

• Produce concise notebooks & dashboards that can feed automated PDF reports.

   

Must‑have qualifications

• 4 – 6+ years in data science, risk analytics, or credit‑modeling.
• Strong Python (pandas, NumPy, scikit‑learn) and SQL; solid PySpark on distributed data a big plus.
• Hands‑on experience building or validating credit‑risk or fraud models (PD, scorecards, Basel/IFRS 9, etc.).
• Fluency in statistics (inferential tests, multicollinearity, model monitoring).
• Git workflow, code review discipline, and comfort with Agile/Kanban boards.
• Clear written & spoken English; able to summarize findings for non‑technical stakeholders.

Nice‑to‑haves

• Familiarity with U.S. mortgage or private‑lending data.
• Experience with Postgres, MinIO/S3, or dbt.
• Knowledge of BI/visualization tools (Plotly, PowerBI, Looker, etc).
• Prior work in a fully remote, internationally‑distributed team.

How we work

• Stack: Python • PySpark • PostgreSQL/Snowflake • GitLab CI • AWS & on‑prem Spark
• Communication: Slack, Zoom, Notion. Meetings kept lean; deliverables drive the schedule.
• Culture: Low‑ego, high‑ownership. We favor clarity, rapid feedback loops, and well‑documented processes.

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

As a Machine Learning Engineer, you'll work as part of the Wix CTO Office team, researching problems that can give Wix’s products a competitive edge across various challenges. A key focus of the team is on Agents over LLMs, exploring new techniques for building agents and developing innovative products that leverage them.  

In your day-to-day, you will:  

• Build POCs for research projects led by the team  
• Evaluate results and provide actionable insights  
• Collaborate with different teams at Wix to advance their agent implementations  

• Build shared infrastructure for agents  

   

Requirements

• Creativity and willingness to tackle ambitious, high-risk problems  
• 3+ years of experience in working on production code with active users  
• BSc in Computer Science or related field, MSc preferred  
• Proficient in Python; TypeScript is a significant advantage  
• Experience in training and evaluating Machine Learning models  
• Hands-on experience with building GenAI systems using LLMs and agents  
• Proven ability to work in a collaborative, cross-functional environment  
• Excellent written and verbal communication skills in English 

About the Team  

We are Wix's Data Science CTO Office team, a small group of researchers and engineers. We collaborate with various groups at Wix and the CEO on innovative research projects. Some projects aim to enhance Wix products with new features, while others focus on strategic research areas that can provide Wix with a competitive advantage.

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.

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.