Jobs Data Scientist

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.

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.

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.

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

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

Data Scientist Intern (Lead Scoring & Models)

🚀 About Us

We help companies identify which nonprofits are worth targeting — and which aren’t.

We already have the data. Now we need to turn it into intelligence.

🧠 The Role

We’re looking for a Data Scientist Intern to build models that turn raw data into actionable insights.

This is a highly applied role — focused on real business impact, not theory.

⚡ What You’ll Work On

• Building lead scoring models
• Developing lookalike models to identify high-fit organisations
• Turning messy data into usable, structured insights
• Exploring predictive approaches (e.g. classification, clustering, KNN, etc.)

🎯 What Success Looks Like

By the end of the internship:

You’ve built models that improve how we prioritise leads and are used in the product or sales workflows.

🧰 What We’re Looking For

• Strong skills in Python and/or SQL
• Experience working with real, messy datasets
• Ability to move from raw data → insight → usable output
• Understanding of modelling approaches (regression, classification, clustering, etc.)

• Builder mindset — you ship, not just analyse

   

✅ Strong Signals

• You’ve built and deployed models in real projects
• You’ve worked with messy or scraped data
• You’ve done end-to-end work (data → model → output)
• You think commercially, not just technically

🧑‍💻 How You’ll Work

• Remote
• High autonomy
• Fast-paced startup environment
• Focus on shipping, not over-analysing

💰 Details

• Paid stipend
• Rolling internship
• Potential to convert to full-time

💥 Why Join

Most data science roles are slow and academic.

Here, you’ll:

• Work on real data with real impact
• Build models that directly affect revenue
• Move fast and own your work
• Learn how data is actually used in a startup

MINT is the leader in Advertising Resource Management (ARM) software, driving marketing transformation in the age of agentic AI. At the core of our innovation is an open enterprise platform that deploys autonomous AI agents to augment human teams across the marketing and advertising value chain, connecting marketing performance with enterprise growth and real business impact.

Thanks to a diverse team of over 300 professionals worldwide, we integrate technology, data, and agentic intelligence to redefine how global brands and agencies plan, execute, and optimize their advertising investments.

With the recent acquisitions of technology leaders Redmill Solutions and info³, MINT has further expanded its unified ecosystem, completing the long-awaited connection of media planning, buying, and execution within one intelligent environment.

The Role

We are looking for a Senior Data Architect — a strategic, hands-on, and forward-thinking leader passionate about reshaping complex data ecosystems, who will drive the transformation of our ARM platform by unifying multi-tenant data models, enabling scalability at growing volumes, and designing a foundation for future modules such as Financial Operations and Compliance.

This is a consultancy engagement, not an employment contract. The selected consultant will work independently, bringing their specialized knowledge and experience to our projects.

What you'll be doing:

Target Architecture Definition

• Conduct a comprehensive audit of the existing data landscape
• Assess current data models, data flows, and technical gaps (scalability, consistency, traceability)
• Evaluate build vs. refactor vs. reuse strategies for ARM 2.0
• Define target architecture principles aligned with product and business goals

Unified Data Modelling

• Design a shared, scalable multi-tenant data model
• Enable deep client configurability without fragmenting the core schema

Orchestration Architecture

• Architect APIs, event-driven systems, and caching layers
• Design end-to-end data flows (inbound and outbound) across multiple data sources and media platforms
• Ensure full traceability of media investments across the value chain (planning → buying → finance → reconciliation)
• Guarantee performance, consistency, and seamless integrations (e.g., SAP/ERP, third-party media tools)

AI/ML Foundations

• Prepare the platform for advanced analytics and AI-driven capabilities
• Support use cases such as: Media data quality scoring, anomaly detection, allocation recommendations, incentives management

This role will be a great fit if you have:

• Strong experience in data architecture for B2B multi-tenant platforms
• Proven ability to design highly configurable systems without compromising data integrity
• Solid understanding of trade-offs between normalization, performance, and maintainability
• Experience working with complex data ecosystems and integrations
• Familiarity with media, advertising, or adtech ecosystems (agencies, media buying, platforms) is a plus

We would also appreciate:

• Advanced data modelling (conceptual, logical, physical)
• API and modular data services design
• ERP integrations
• MDM and multi-tenant architecture
• Cloud data platforms
• Data governance and privacy-by-design
• Applied ML experience

Interview Process

Candidates can expect the following steps in our interview process:

• HR Video Interview (30 minutes): A virtual interview with HR to provide an overview of the company and the role, as well as to gain insight into your experience and background.
• Technical Interview with Tech team (1 hour): A virtual interview with the VP Engineering or other Team members, focused on your technical expertise, past experience, and problem-solving approach.
• Final Interview with the CPTO (30 min)
• Offer: after completing the interview process, successful candidates will receive an offer.

MINT is committed to a diverse and inclusive workplace. MINT AI is an equal opportunity employer and does not discriminate on the basis of age, race, religion, color, national origin, gender, gender identity, gender expression, sexual orientation, immigration status, medical condition, protected veteran status, disability, genetic information, political views or activity, or other legally protected status.

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.

We're seeking an exceptional Data Architect to lead data storage strategy and design across our multi-product SaaS platform as we unify independent products into a coherent platform. This role combines strategic leadership with hands-on technical expertise—you'll shape our data architecture vision while getting into the details during implementation and troubleshooting.

Key Responsibilities: 
•⁠ ⁠Drive data architecture unification across multiple SaaS products, creating coherent patterns while respecting product needs.
•⁠ ⁠Partner with vertical technical leads to provide horizontal architectural support and alignment.
•⁠ ⁠Design and optimize data storage across multiple technologies—AWS OpenSearch/Elasticsearch, relational databases, S3, NoSQL, and data warehousing.
•⁠ ⁠Optimize for performance and resilience— indexing, querying, high availability, redundancy, and disaster recovery at scale.
•⁠ ⁠Support AI initiatives—partner with our AI specialist team on data architecture for AI capabilities.
•⁠ ⁠Bring clarity from ambiguity—translate complex challenges into clear architectural direction.
•⁠ ⁠Build capability, not dependencies—mentor engineers so teams become more self-sufficient.
•⁠ ⁠Balance performance, cost, and reliability across our platform.

Qualifications:
•⁠ ⁠Deep expertise in AWS OpenSearch/Elasticsearch—you've solved hard problems at scale with indexing, querying, and performance.
•⁠ ⁠Broad data technology experience—relational databases (e.g. PostgreSQL, MySQL, RDS), object storage (S3), NoSQL (e.g. DynamoDB, MongoDB, Redis), data warehousing.
•⁠ ⁠Cloud and container knowledge—AWS preferred (Azure/GCP also valued); understand stateful services in Kubernetes.
•⁠ ⁠Proven platform architecture experience—track record of driving technical strategy across multiple teams or products at scale.
•⁠ ⁠Senior-level maturity—you make those around you better through mentorship and capability building.
•⁠ ⁠Influence without authority—you work effectively across teams, bring clarity from ambiguity, and think strategically.
•⁠ ⁠SaaS experience—comfortable with multi-tenant environments and operational excellence.

Location: EU or Ukraine
Type: Fulltime 
Start: ASAP

Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
The company is a trailblazer in the world of data-driven advertising, known for its innovative approach to optimizing ad placements and campaign effectiveness through advanced analytics and machine learning techniques. Our mission is to revolutionize the advertising sector by enabling brands to reach their audiences more effectively.

About the role:
We are seeking an experienced and motivated Senior Data Scientist to join our dynamic team. The ideal candidate will have deep expertise in supervised learning, reinforcement learning, and optimization techniques. You will play a pivotal role in developing and implementing advanced machine learning models, driving actionable insights, and optimizing our advertising solutions.
This position is based in Ukraine. The team primarily works remotely, with occasional in-person meetings in the Kyiv or Lviv office.

Responsibilities:
- Develop and implement advanced supervised and reinforcement learning models to improve ad targeting and campaign performance.
- Collaborate with cross-functional teams to identify opportunities for leveraging machine learning and optimization techniques to solve business problems.
- Conduct extensive data analysis and feature engineering to prepare datasets for machine learning tasks.
- Apply optimization algorithms to enhance the effectiveness and efficiency of advertising campaigns.
- Evaluate and refine existing models to enhance their accuracy, efficiency, and scalability.
- Utilize statistical techniques and machine learning algorithms to analyze large and complex datasets.
- Communicate findings and recommendations effectively to both technical and non-technical stakeholders.
- Stay updated with the latest advancements in machine learning, reinforcement learning, and optimization techniques.
- Work with engineering teams to integrate models into production systems.
- Monitor, troubleshoot, and improve the performance of deployed models.
- Mentor junior data scientists and contribute to the continuous improvement of the data science practice within the company.

Requirements:
- 5+ years of experience in data science or machine learning roles, with a strong focus on supervised learning, reinforcement learning, and optimization techniques.
- Technical Skills:
- Proficiency in Python.
- Strong understanding of working with relational databases and SQL.
- Experience with machine learning libraries such as scikit-learn, TensorFlow, PyTorch, or similar.
- Deep understanding of statistical modeling and supervised learning algorithms (e.g., linear regression, logistic regression, decision trees, random forests, SVMs, gradient boosting, neural networks).
- Hands-on experience with reinforcement learning algorithms and frameworks like OpenAI Gym.
- Practical experience with optimization algorithms (linear, non-linear, combinatorial, etc.).
- Hands-on experience with data manipulation tools and libraries (e.g., pandas, NumPy).
- Familiarity with cloud services, specifically AWS, is a plus.
- Practical experience building and managing cloud-based ML pipelines using AWS services (e.g. SageMaker, Bedrock) is a plus.
- Education:
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related field. A PhD is a plus.

Other Skills:
- Strong analytical and problem-solving skills.
- Excellent communication skills, with the ability to clearly articulate complex concepts to diverse audiences.
- Ability to work in a fast-paced environment and manage multiple priorities.
- Strong organizational skills and attention to detail.
- Ability to mentor and guide junior data scientists.
- Must be able to communicate with U.S.-based teams

The company offers:
- An opportunity to be at the forefront of advertising technology, impacting major marketing decisions.
- A collaborative, innovative environment where your contributions make a difference.
- The chance to work with a passionate team of data scientists, engineers, product managers, and designers.
- A culture that values learning, growth, and the pursuit of excellence.

We’re looking for a Senior Game Mathematician — someone who sees patterns where others see chaos, and who understands that behind every “big win” there’s elegant math. You’ll shape the core of our games: design math models, define volatility and player experience, and turn ideas into numbers that actually feel right.

We’re a team of people who left safe jobs to build something better. No bureaucracy, no pointless meetings — just sharp minds building games that stand out.

🎲 What you’ll do

• Design and develop math models for slot games — from raw concept to release;
• Prototype and simulate gameplay mechanics to test balance, volatility, hit rates, and potential;
• Reverse-engineer competitors’ math to understand and improve on the best mechanics in the market;
• Collaborate with producers and developers to bring your math to life — ensuring technical and creative integrity;
• Run simulations, build reports, and validate results in Excel or via your own scripts/tools;
• Prepare clear documentation for internal reviews, certification, and release;
• Analyze performance data of live games, providing actionable insights for tweaks and next iterations.

🧠 What you bring

• 3+ years of experience in slot game math design (or similar gambling / probability-based products);
• Advanced Excel and solid programming/scripting knowledge (Python, R, or similar);
• Deep understanding of RTP, volatility, hit frequency, max exposure, feature math, and player behavior;
• Obsessive attention to detail — and ability to explain complex systems clearly;
• Collaborative mindset: you enjoy working with creative and technical people to make something that just feels good;
• Fluent English.

💎 Nice to have

• Experience with internal math simulators or your own simulation tools;
• Familiarity with regulatory / certification requirements for game math;
• Curiosity for game design and player psychology — not just numbers.

🎁 Benefits

• 21 vacation days + 5 extra day-offs annually;
• 12 paid sick days;
• Fully remote format — work from anywhere you feel productive;
• Flexible schedule: start your day anytime between 08:00–12:00 CET;
• Fixed budget for health insurance and gym/fitness;
• Provided all required work equipment;
• Zero bureaucracy and direct communication with founders and C-level;
• Minimal meetings, async-friendly workflow;
• Startup energy: fast motion, creativity, and tight-knit communication;
• Business trips and team meetups several times per year;
• Multiple salary payout options (flexible formats);
• …And many more perks unlocked after we hit break-even.

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

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

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

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

Job Responsibilities:

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

Requirements:

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

Skills:

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

Nice to have:

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

What We Offer:

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

Who we are:

Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries.

About the Product:

Our platform is a cloud-based AI-driven workspace that automates statistical analysis validation and generation for clinical research. It serves large pharmaceutical and biotech clients by extracting, validating, and producing complex tabular outputs and regulatory deliverables. 

The system handles high volumes of hierarchical tables, figures and listings, applying both classical and generative NLP to accelerate review cycles, reduce manual double-programming and maintain a full audit trail. 

About the Role:

As a Middle Data Scientist, you will support the development of novel AI and algorithmic approaches—particularly in NLP and tabular data—to enhance our core platform. You will take hypotheses through proof-of-concept, explore complex clinical datasets, identify patterns, evaluate alternative methods, and recommend optimal solutions. 

Key Responsibilities:

• Design, develop, and prototype NLP algorithms (classical and generative) for document parsing, NER, classification, and table generation

• Implement tabular data models and pipelines for metadata extraction, transformation, and validation

• Work with large, heterogeneous clinical datasets: clean, preprocess, and structure data for analysis

• Evaluate multiple algorithmic approaches, benchmark performance, and select optimal solutions

• Maintain reproducible code, thorough documentation, and version control for research assets

Required Competence and Skills:

• 2- 4 years of industry experience in NLP and/or tabular data processing
• 1+ years of hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow)
• Proven track record of delivering AI-driven projects into production
• Expertise in classical NLP techniques and generative methods (e.g., text-to-SQL, generative tabular models)
• Strong proficiency in Python and associated data science libraries
• M.S. or Ph.D. in Computer Science, Data Science, Statistics, or related quantitative field
• Excellent English communication skills, both written and verbal

Nice to Have:

• Experience with cloud-based NLP platforms or managed AI services

Why Us:

We provide 20 days of vacation leave per calendar year (plus official national holidays of a country you are based in).

We provide full accounting and legal support in all countries we operate.

We utilize a fully remote work model with a powerful workstation and co-working space in case you need it.

We offer a highly competitive package with yearly performance and compensation reviews.

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.

Working conditions

5-day working week, 8-hour working day;

Remote work.

Role summary
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.
 

What you will do

• 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.

Success metrics

• Improved Engagement score
• Measurable uplift in campaign ROI
• Reduced time-to-insight
• Increased share of HCPs with actionable recommendations
• Improved coverage/quality signals (e.g., NPI resolve rate progress, event capture reliability)

Requirements (must-have)

• 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.

Soft skills & values (required)
Openness

• Direct feedback, acknowledges mistakes, listens and acts on critique.

Internal Locus

• Takes responsibility for outcomes, analyzes own decisions first, learns quickly from failures.

Get it done

• Delivers end-to-end, removes blockers independently, completes work to production-grade finish.

Self-improvement

• Continuously adopts new tools (incl. AI), improves workflows, increases productivity without heavy training overhead.

Ownership

• Treats the product and system results as their responsibility, not just personal tasks/KPIs; minimizes need for micromanagement.