Jobs Data Science

ZentixSoft is looking for a Data Architect! 🚀

Format: Full-Time Outstaffing, long-term contract (12 months), 100% Remote.

💡 Why ZentixSoft?
 💸 Salary Review: A structured system of compensation reviews aligned with your engineering growth. 
⚖️ Work-Life Balance: Sustainable workflows with no senseless overtime, helping you keep your resource fully charged. 
🦾 Trust and Transparency: Zero micromanagement and no invasive time-trackers—we fully trust your professional autonomy. 
🎁 Gifts & Culture: Corporate gifts for holidays, birthdays, and a true team atmosphere where your voice always matters.
 

🧩 Role Overview & Responsibilities:

We are looking for a hands-on Data Architect to take full architecture ownership of a complex data platform. In this role, you will assess, stabilize, improve, and modernize the existing architecture, delivering rapid, actionable engineering recommendations.

• Architecture Ownership: Define tactical (immediate stabilization) and target (long-term modernization) architectures that engineering teams can directly execute.
• Platform Modernization: Assess complex, legacy data environments; identify performance/scalability bottlenecks; and deliver clear, implementation-ready architectural outputs.
• Unstructured & Structured Data: Architect data pipelines and modeling frameworks capable of handling unstructured data (documents, PDFs, free text, scanned content, images) and structured/file-based datasets.
• Tech-Neutral Evaluation: Evaluate enterprise technologies based on scale, cost, workload, and business requirements rather than preference (determining when Databricks, Synapse, ADF, or Azure SQL are truly justified).
• Data Modeling & Pipelines: Design robust ETL/ELT frameworks, dimensional schemas, star schemas, and incremental processing/refresh strategies.
• Documentation & Governance: Create detailed architecture diagrams, technical decision logs, and data governance standards.
   

🎓 Our Perfect Match (Requirements):

• Proven Architecture Ownership: 4+ years of recent, demonstrated experience as a Data Architect owning overall Azure Data Platform architectures (candidate must be an Architect, not solely a Data Engineer).
• Azure Data Platform Expertise: Extensive hands-on background with Azure Data Services, including Azure SQL / SQL Server, Azure Data Lake, Azure Data Factory (ADF), Azure Synapse, and Databricks.
• Azure Cosmos DB (MUST-HAVE): Hands-on experience working with Azure Cosmos DB and file-based data stores within enterprise environments.
• Data Engineering Core: Strong mastery of Python, SQL, ETL/ELT, and Lakehouse architectures.
• Unstructured Data Experience: Proven track record handling unstructured data sources (documents, PDFs, text, scanned files).
• Fast Delivery & Problem Solving: Ability to onboard quickly into complex environments, navigate incomplete documentation, and deliver meaningful architectural guidance rapidly.
• Communication & English: Fluent English (C1) with exceptional technical writing and stakeholder communication skills.
   

📍 Project Details:

⏳ Duration: 12 Months (Full-time, 100% Remote).

🕒 Time Zone: CET / CEST.
 

We look forward to receiving your CV (please ensure your CV details your project contexts, architecture ownership, and hands-on Cosmos DB/Azure experience).

Are you a Data Scientist with a love of LLMs, generative AI?

We are looking for a passionate Data Scientist to implement AI solutions aimed at achieving business goals.

This role offers the opportunity to work on cutting-edge AI adoption projects that helps to improve current business processes.

You'll be a great fit if you have:
• Strong Python Experience (1 year +);
• Experience with LLM , Diffusion models (1 year +);
• Knowledge of Prompt engineering;
• Experience with Gen AI-related technologies such as LangChain and RAG;
• Experience with Neural Networks (Optional) ;
• Experience with NLP , Predictive analytics and Machine learning;
• Experience with Pandas;
• Experience with SQL, including experience with large datasets;
• Strong experience in statistics;
• Bachelor's degree in Computer Science or a related field.

What you'll do:
• Develop AI agents that utilize LLM, RAG and langchain approach;
• Implement LLM and Diffusion models to boost business productivity;
• Utilize LLM (LLM Vision) to improve object detection, text classification and extraction;
• Create forecasting, recommendation, and classification models;
• Transform business challenges to AI applications;

We ensure your growth with:

• Competitive salary fixed in USD;

• Flexible working schedule and fully remote work format;

• Paid vacation days and sick leave days;

• Personal and professional development opportunities;

• Participation in building innovative projects from scratch using modern technologies;

• Team-building activities and corporate events;

• English classes and educational events.

RedCore is an international business group that creates technological solutions for digital markets. Our products and services cover fintech, marketing, e-commerce, customer service, communications, and regulatory technologies.

We are looking for a Senior Data Scientist to join our team!  

Requirements: 
- Proven experience in analysis large amount of data;
- Experience in solving customer behaviour prediction tasks;
- Knowledge of ML algorithms for regression, classification, clustering, ability to apply them to business problems;
- Solid experience in SQL;
- Extensive experience with Python for data processing, modeling, and visualization.;
- Deep understanding of statistics, probability theory;
- Passion to drive projects from R&D to business value. 

Will be a plus: 
- Familiarity with AWS infrastructure and toolchain (SageMaker, CloudFormation, CloudWatch, etc.);
- Familiarity with BI tools (Metabase, Tableau);
- Experience with tools for managing ML workflows;
- Knowledge and experience with algorithms and data structures;
- Knowledge of OOP;
- Understanding of containerization technologies (Docker)
participation in Kaggle competitions. 

Responsibilities: 
- Develop and deploy predictive models for Customer Lifetime Value (LTV) to optimize marketing spend and improve retention strategies
- Developing and testing machine learning models for customer behaviour prediction, deploying to production;
- Working with tabular data - collecting, cleaning, and exploring datasets, building ML pipelines;
- Applying statistical methods to analyze and interpret data, finding patterns, insights to improve the quality of models;
- Maintaining existing models;
- Communicating and presenting results. 

Our benefits to you:

☘️An exciting and challenging job in a fast-growing business group, the opportunity to be part of a multicultural team of top professionals in Development, Architecture, Management, Operations, Marketing, Legal, Finance, and more

🤝🏻Great working atmosphere with passionate experts and leaders, sharing a friendly culture and a success-driven mindset is guaranteed

🧑🏻‍💻Modern corporate equipment based on macOS or Windows and additional equipment are provided

🏖Paid vacations, sick leave, personal events days, days off

💵Referral program — enjoy cooperation with your colleagues and get the bonus

📚Educational programs: regular internal training sessions, compensation for external education, attendance of specialized global conferences

🎯Rewards program for mentoring and coaching colleagues

✈️In-house Travel Service

🦄Multiple internal activities: online platform for employees with quests, gamification, presents and news, clubs for movie / book / pets lovers and more

🎳Other benefits could be added based on your location 

About the client

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

About the role

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

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

Responsibilities:

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

Requirements:

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

Join us to develop SLAM, Visual-Inertial Odometry (VIO), and state estimation solutions for real-world robotics and navigation systems.

We're looking for an engineer with a strong mathematical background and extensive experience in classical and geometric computer vision. You'll independently design, prototype, and optimize computer vision algorithms, quickly turn research ideas into working solutions, and contribute to technical decisions across the development process.

Responsibilities:

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

Required Qualifications:

• 5+ years of professional experience in computer vision, robotics, or a related field
• Deep understanding of linear algebra, statistics, and optimization methods
• Strong hands-on experience with classical and geometric computer vision
• Strong understanding of camera models, feature detection and tracking, geometry, and state estimation
• Proven experience developing SLAM, Visual-Inertial Odometry (VIO), or related localization systems
• Strong C++ development skills and experience with Linux
• Experience designing, implementing, and optimizing production-ready computer vision algorithms
• Ability to independently investigate research papers and implement state-of-the-art methods
• Experience working on R&D projects
• Degree in Computer Science, Mathematics, Physics, Engineering, or related STEM field

Nice to Have:

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

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

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

Required Qualifications

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

Nice to Have

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

We are looking for a Data Scientist to join a risk modeling team within one of the leading banks in Ukraine. The team has been a benchmark in risk modeling for over 10 years, applying advanced Machine Learning techniques and global best practices.
 

We are open to considering candidates from Junior to Senior level, depending on experience and expertise.
 

In this role, you will develop ML solutions to minimize financial risks and optimize strategies for working with millions of customers.
 

Why Join the Team:

• Technology Stack: We use cloud solutions and modern development tools such as Amazon SageMaker, Amazon S3, Git, Confluence, etc.
• Scale: Working with large-scale datasets to support critical business decisions.
• Expertise: Risk models aligned with international standards and regulatory requirements.
• Professional Community: Strong Data Science community, regular internal workshops, participation in conferences, and sponsored learning.
• Automation: Proprietary system for managing the full ML lifecycle (from development to monitoring).
   

Key Requirements:

• 1+ year of experience in Data Science (experience in FinTech/Banking will be a strong advantage);
• Strong proficiency in Python for data analysis (NumPy, Pandas, SciPy, etc.) and experience optimizing SQL queries on large datasets;
• Hands-on experience with ML frameworks such as Scikit-learn and gradient boosting libraries (LightGBM, XGBoost, etc.);
• Experience with time series analysis and forecasting tools (Statsmodels, Prophet, etc.);
• Solid understanding of mathematics behind models (from linear regression to gradient boosting) and model evaluation techniques;
• Understanding of Big Data concepts and experience with version control tools (Git).
   

Nice to Have:

• Experience with Docker and/or MLflow;
• Knowledge of financial regulations (e.g., Basel III, IFRS 9).
   

Key Responsibilities:

• Credit Risk Modeling: Develop portfolio risk models (PD, LGD, EAD) in line with IFRS 9 requirements;
• Income & Solvency Forecasting: Build ML models to assess customer financial condition based on transactional activity;
• Credit & Behavioral Scoring: Develop classical and advanced scoring systems for automated decision-making across the customer credit lifecycle;
• Market Risk Analysis: Apply time series analysis and ML techniques to assess liquidity risk, forecast FX volatility, and model customer fund behavior;
• Fraud & Anomaly Detection: Build and maintain systems for detecting unusual transaction patterns in real time;
• ML Lifecycle: Participate in the full lifecycle of models — from idea and data collection to automated monitoring and retraining.
   

What We Offer:

• Opportunity to work in one of the leading banks in Ukraine;
• Official employment and 24+4 calendar days of vacation;
• Sick leave compensation;
• Competitive salary;
• Bonuses and performance-based incentives;
• Medical insurance;
• Corporate training and development programs;
• Remote work options;
• Financial support in critical situations.

We are looking for a Data Scientist to join our Trading Core and Risk Tech squads and drive data-informed decision-making across our trading systems. In a high-frequency trading environment processing billions in volume, success depends on precision, robust analytics, and the ability to turn complex data into actionable insights.

In this role, you will research, model, and validate the core data-driven models that drive our pricing engines, automated market-making algorithms, risk management frameworks (A/B/C-book optimization), and liquidation mechanics. You will sit at the intersection of statistics, data science, and high-performance software engineering, working directly with Core Product Managers and engineering teams to turn complex financial data into proprietary algorithmic advantages.

Responsibilities

• Research, design, and prototype behavioral, risk, and toxicity-scoring models for client and partner-flow segmentation
• Build backtesting and monitoring frameworks to validate models, signals, and hypotheses across data of varying granularity
• Detect early risk signals, anomalies, and regime shifts in market and client behavior, including probability of critical capital loss
• Develop explainable risk signals and labels for the R&D team, and long-term client value/risk models with forecasting
• Conduct research and hypothesis-testing on client economics, flow quality, and model performance

Requirements

• 4+ years of experience as a Data Scientist  
• Exceptional knowledge of probability theory, statistics, time-series analysis, and financial mathematics
• Advanced proficiency in Python (NumPy, Pandas, SciPy, Scikit-learn, Statsmodels) for data analysis, modeling, and backtesting
• Solid experience with Machine Learning
• SQL skills and experience working with large-scale historical market data (tick data, order logs)
• Strong logical thinking, initiative, and well-developed communication skills

Will be a plus

• Experience in CFD, Crypto CEX, Prop Trading Firm, or Hedge Fund
• Degree (MSc or PhD preferred) in a highly quantitative field: Mathematics, Physics, Statistics, Quantitative Finance, or Computer Science
• Deep understanding of market microstructure, order book dynamics, risk metrics (VaR, Expected Shortfall), and margin/liquidation mechanisms
• Experience with AI (Claude.io, Copilot, Codex)

We offer

• 20 paid vacation days per year
• 10 paid sick leave days per year
• Public holidays as per the company's approved Public holiday list
• Medical insurance
• Opportunity to work remotely
• Professional education budget
• Language learning budget
• Wellness budget (gym membership, sports gear and related expenses)

Domain: Banking & Finance
Experience: 10+ years
Project Duration: September 1, 2026 – June 29, 2027
Format: Remote
Location: Poland

About the Project

We are looking for a Data & Machine Learning Architect to join a greenfield initiative focused on building a next-generation, cloud-native digital banking platform.

The project aims to create a scalable, modular, API-first, event-driven, and AI-powered ecosystem supporting real-time operations, faster product innovation, and sustainable business growth.

The platform will leverage modern Microsoft Azure services, advanced data technologies, and machine learning capabilities to enable digital transformation at scale.

Responsibilities

• Define and design end-to-end data and machine learning architectures using Microsoft Azure
• Lead the design and implementation of modern data platforms covering data lakes, data warehouses, real-time streaming, and analytics
• Develop scalable ML pipelines covering data ingestion, model training, deployment, monitoring, and automated retraining
• Establish MLOps practices, CI/CD standards, governance frameworks, and best practices across data and AI environments
• Ensure data quality, security, regulatory compliance, reliability, and operational excellence across the platform
• Collaborate with business and technical stakeholders to translate business requirements into robust and scalable technology solutions
• Provide architectural guidance, technical leadership, and mentorship to engineering teams
• Evaluate emerging technologies and contribute to the strategic roadmap for data and AI capabilities

Requirements

• 10+ years of professional experience across Data Engineering, Data Architecture, Analytics, Artificial Intelligence, or Machine Learning
• Proven experience designing and delivering enterprise-scale data and machine learning platforms within the Microsoft Azure ecosystem
• Advanced knowledge of Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Data Lake Storage, Azure Machine Learning, AKS, and Azure DevOps
• Practical experience implementing MLOps practices, including ML lifecycle management, model governance, monitoring, and CI/CD automation
• Strong understanding of machine learning workflows, feature engineering, model deployment, and responsible AI principles
• Solid expertise in data governance, security, regulatory compliance, and metadata management
• Strong hands-on programming skills with Python and SQL
• Experience with containerization and orchestration technologies such as Docker and Kubernetes
• Strong leadership, communication, collaboration, and stakeholder management skills
• Fluent business and technical English

Tech Stack

• Data Engineering
• Data Analysis
• AI / Machine Learning
• Microsoft Azure
• Azure Data Factory
• Azure Databricks
• Azure Synapse Analytics
• Azure Data Lake Storage
• Azure Machine Learning
• Azure DevOps
• CI/CD
• Python
• SQL
• Docker
• Kubernetes

Project Details

• Start date: September 2026
• Duration: 10 months
• Project type: Greenfield
• Verification process: Technical interview + project interview

Position Overview

We are seeking an experienced Data Scientist/ML Engineer specializing in neural networks, with 3+ years experience in the applied field. The ideal candidate should be a great mentor and team player; python ninja with a strong background in various neural network architectures (recurrent, convolutional, feedforward, etc.) and a deep understanding of developing, tuning, and deploying different architectures. This role also requires excellent Python programming skills and a solid foundation in data analysis and statistics. If you strive in a fast pace environment, with tight deadlines but fast professional growth, you are welcome in our team!

Key Responsibilities:

• Create statistical summaries to support hypothesis testing and data-driven decisions during EDA
• Implement data preparation and feature engineering pipelines for the models
• Plan and implement algorithms for predictive modeling.
• Provide continuous improvement of models in production
• Lead the ML-driven python coding for end to end model deliveries, support our ML-related libraries/
• Collaborate with a cross-functional team of specialists, including senior and junior data scientists, Python developers and ML engineers, data analysts, and DevOps specialists.
• Provide expertise in tuning loss functions, metrics, sample weights adjustment, hyperparameter tuning and model improvement in general.
• Generate detailed evaluation reports, summarizing key findings and presenting actionable insights to stakeholders.
• Keep up with tight deadlines, agile environment of work with evolving objectives and KPIs, having the highest level of organisation and self management to provide a full work clarity, extensive tracking and documentation of your work.

Required Skills and Qualifications:

• Minimum of 3 years of professional experience as a Data Scientist or Machine Learning Engineer.
• Proficient in building custom neural networks using PyTorch.
• Strong experience with various neural network architectures specialised for time-series modelling and tabular data predictive modelling 
• Proficiency with neural network layers (like LSTM, GRU, Conv1D, Dense, Dropout, BatchNormalization) and activation functions (ReLU, Sigmoid, Tanh, Softmax).
• Experience with time series data, including loading, cleaning, and feature engineering using libraries like pandas, numpy. Knowledge of Darts library would be a large bonus.
• Expertise in model development, including selecting and developing models from the Darts library and building custom neural network architectures.
• In-depth knowledge of Python programming and relevant libraries (e.g., Scikit-learn, Pandas, PyTorch, Shap). We want to emphasize this requirement of excellency in python, including good working OOP knowledge. We do noе accept candidates with partial experience in python, as our projects require solid programming skills for writing and supporting data science related functionalities and libraries
• Solid understanding of statistics and data analysis.
• Skilled in using and tuning loss functions such as MSE, MAE, Cross-Entropy Loss, and custom loss functions relevant to time series forecasting.
• Experience with hyperparameter optimization using libraries like Optuna or Hyperopt.
• Capable of implementing and optimizing training loops in PyTorch, including data loaders and training validation steps.
• Knowledge of time series-specific cross-validation techniques and regularization methods to prevent overfitting.
• Strong skills in evaluating model performance using metrics like MSE, RMSE, MAE, MAPE, and R-squared.
• Familiarity with backtesting functionalities for validating model performance.
• Experience in model deployment, including serialization and deployment of models for real-time services.
• Strong communication skills for presenting findings and progress to stakeholders, using visualizations created with matplotlib, seaborn, or plotly.
• Proficiency in using Git for version control and collaboration.
• Major education in Data Science, Computer Science, or a related field IS A MUST. We do not accept people with other backgrounds.
• Certifications in deep learning are preferred.
• Excellent command of spoken and written English.
• Strong self-management, time management, and organizational skills.

• Ability to work under a structured management system while independently handling creative tasks.

   

What We Offer:

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

About DataObrii
DataObrii is a high-tech consulting firm specializing in data science, machine learning, and AI-augmented Internet of Things. Our team comprises experienced data scientists, python engineers, devops, hardware electrical engineers, and business analysts dedicated to delivering innovative, data-driven solutions that enhance business intelligence and efficiency. We emphasize continuous improvement, keeping up with current and emerging technologies, and delivering complete timely, effective solutions.

Our Values:

• Efficiency: We employ an agile approach to ensure timely delivery of high-quality solutions. This governs fast delivery cycles, quick and efficient solutions, iteratively going from PoC developments to fully enhanced production systems.
• Professionalism: Our commitment to excellence drives us to achieve success for our clients. This includes both technical and ethical proficiency required from all our employees.
• Creativity: We utilize design thinking to develop innovative solutions that address complex business challenges.
• Care: We invest time in understanding our clients' business models to provide tailored solutions that align with their objectives.
   

We are looking for a Quant Researcher to take ownership of systematic trading strategies across crypto markets.

You will be expected to take ideas from hypothesis through data analysis, backtesting, execution design and live trading — and ultimately take responsibility for PnL.

What you will own

• Research of short-horizon and trading opportunities
• Market microstructure analysis 
• Strategy design and validation
• Execution logic and live performance analysis
• Strategy monitoring and iteration after deployment

What we care about

We care substantially more about how you think than about your job title. Strong candidates will typically have experience with some combination of:

• systematic / quantitative trading
• crypto market microstructure
• high-frequency or short-horizon signals
• lang for research
• large market datasets
• realistic backtesting and simulation
• execution analysis
• live trading systems

Compensation and ownership

This is a performance-driven role. Your upside is directly linked to the live performance of the strategies you research, deploy and own.

Strategies that pass validation receive a meaningful initial capital allocation and are scaled based on live performance, capacity and risk.

Meaningful contributions to the boutique’s infrastructure may be compensated separately on a fixed basis.

Before applying:

• Market microstructure knowledge is a hard requirement. If you cannot reason confidently about it, please do not apply.
• You must be comfortable designing and validating strategies operating on sub-second horizons.
• Generic, templated or LLM-generated technical answers presented as your own work will result in immediate rejection.
• Applications that do not meet these requirements may be rejected without individual feedback.

This role is not suitable for candidates whose experience is limited to OHLC-based strategies.

Playtika is looking for a Data Scientist Expert — Production ML to join the Data & AI department

In this position you will join a multidisciplinary team focused on personalization using Reinforcement Learning methods. Over the last few years, we have built a real-time recommendation engine based on Bayesian Multi-Armed Bandits — including our open-source PyBandits library — serving millions of players across multiple studios and use-cases.

This is an applied research role, with impact measured in production. Your primary contribution will be advancing the mathematical and statistical foundations of our existing solutions: identifying limitations, deriving principled improvements, and owning those improvements all the way through to production. You will not be handed research problems from above — you will find them yourself, inside systems that are already running at scale.

Scaling here is fundamentally a mathematical problem: identifying better statistical methods, proving they hold under real-world constraints, and knowing exactly how far they deviate from the ideal. That’s what sets this role apart.

Responsibilities

Identify mathematical and statistical limitations in existing ML solutions and deliver provably correct, more efficient alternatives
Validate the correctness of scaled solutions — diagnosing where distributed execution or approximation invalidates prototype-level results, and redesigning accordingly
Own the full lifecycle of your improvements: from research and prototyping through production deployment and impact measurement
Drive the evolution and scalability of our personalization and recommendation systems to meet future business and technical requirements
Contribute to end-to-end data-driven research including problem definition, data collection, model development, evaluation, and deployment
Collaborate with cross-functional teams (engineers, BI, Product) to deploy solutions quickly and effectively
Mentor Data Scientists and ML Engineers, and serve as a technical reference on statistical and mathematical rigor across the organization

Qualifications

M.Sc or PhD in Computer Science, Mathematics, Statistics, Engineering or a related field
5+ years of relevant working experience, including significant production ML ownership
Demonstrated experience taking statistically sophisticated models from prototype to production and operating them successfully under real-world business constraints.
Experience with neural network architectures, including the ability to diagnose, interpret and debug their behavior in probabilistic or decision-making contexts (e.g. Bayesian Neural Networks, Neural Linear models)

Strong theoretical foundation in at least one of: Multi-Armed Bandits, Bayesian methods, online learning, recommendation systems, or related decision-making frameworks
Strong understanding of experimentation, causal inference and statistical evaluation of online decision systems.
Strong hands-on Python engineering skills, with the ability to design, implement, review, and maintain production-quality code, leveraging modern AI coding assistants (e.g., Claude, Cursor) to improve productivity and code quality
Proficiency in SQL
Clear written and verbal communication — able to explain statistical reasoning to non-technical stakeholders
Ability to operate autonomously and influence decisions across teams without direct authority.

Nice to have

Experience with probabilistic programming frameworks (e.g. PyMC or Numpyro)
Familiarity with MLOps tooling (experiment tracking, model serving, monitoring)
Familiarity with approximate inference methods (variational inference, MCMC)
Experience with PySpark
Experience in the Mobile Gaming industry

Remote – Full-time – Flexible Hours - Shifted schedule to EST (New York) timezone

About TenViz

TenViz Predictive Analytics builds AI-powered solutions that help Fortune 100 investors anticipate market shifts before they become consensus. We combine financial expertise, mathematical rigor, and AI research to deliver predictive analytics for institutional investors.

Your Expertise

• Strong interest in quantitative and algorithmic approaches
• BSc/MSc in Math, Physics or related field with solid background in statistics, linear algebra and probability theory
• Advanced Linear Algebra, Advanced Statistics, Using Linear Algebra methods in Statistical analysis
• 2+ years in data research, quantitative modeling, ML prototyping, and backtesting
• Skilled in Python, SQL, Git
• Experience with  experiment tracking, pipeline orchestration and workflow automation

Nice to have:

• Bayesian inference, reinforcement learning, or ensemble methods
• Financial econometrics, time-series forecasting, or regime modeling

What You’ll Do

• Design predictive models using probabilistic, ML, and hybrid approaches
• Analyze large-scale financial datasets to detect hidden patterns and early regime shifts
• Prototype and evaluate algorithms, from deep learning to Bayesian models
• Write production-grade research code in Python/SQL that integrates into pipelines
• Translate results into insights and themes investors can act on — not just outputs

Example projects:

• Forecasting inflation via commodity price signals
• Constructing probabilistic forecasts of currency moves around macroeconomic announcements
• Designing a Bayesian model to estimate recession probabilities from yield curve dynamics
• Designing a regime-aware ML pipeline that switches models depending on market conditions
• Creating hybrid ensembles that outperform traditional benchmarks

What’s in It for You

• Remote-first, flexible hours
• Paid vacation & holidays (after 8 months)
• Continuous learning: advanced ML/DL workshops, probabilistic methods, optimization
• Supportive, collaborative team — no micromanagement, focus on trust and creativity
• Work on real financial problems where your models influence billion-dollar decisions

Description

Step into a mission-critical role at the intersection of Generative AI and life-saving technology. We are partnering with a titan of Japanese industry to revolutionise how medical and industrial equipment is managed in hospitals and facilities worldwide. You will be a founding member of a team building a sophisticated GenAI platform designed to provide instant, intelligent access to complex product knowledge—from assembly and configuration to intricate repair procedures. This project is in its infancy, offering you the unique “greenfield” opportunity to act as a primary architect, defining the tech stack and software patterns that will power the next generation of industrial intelligence.

Requirements

Requirements:

Extensive experience in Data Science and Machine Learning with a strong focus on Natural Language Processing (NLP) and Data Engineering.
Proven track record of designing and implementing complex data models for large-scale information retrieval systems.
Expertise in cleaning and structuring highly unstructured data (e.g., audio transcripts, PDFs, manual reports, and sensor data).
Good experience with the Azure cloud ecosystem and its data services.
Hands-on experience implementing Large Language Models (LLMs) and OpenAI capabilities within production environments.
Strong communication skills to work with cross-functional teams and understand the nuances of highly technical documentation.
Nice to Have:

Ability to define and implement software architecture from scratch for ML-driven platforms.
Experience in knowledge graph construction or advanced data linking techniques is preferred.
A background in regulated industries (Healthcare or Industrial) is an advantage.

Job responsibilities

As a Senior ML Data Scientist, your primary focus will be the transformation of vast, unstructured “tribal knowledge” into a high-fidelity digital brain. You will orchestrate the entire data lifecycle, from the initial ingestion of diverse sources—such as captured expert interviews, technical service manuals, and real-time IoT streams—to the final deployment of GenAI agents. Your work involves designing complex data models that can represent intricate relationships between technical components, cleaning and structuring noisy data, and building the pipelines that link these insights together for OpenAI-driven consumption. You will be responsible for defining the architecture of the end solution on Azure, ensuring that the resulting GenAI agents provide accurate, quick, and context-aware responses to technicians and medical professionals on the front lines.

Key responsibilities

• Own end-to-end solution architecture for data, analytics, and AI use cases, with accountability for design quality, security, scalability, supportability, and cost.
• Translate business needs into practical technical designs, target architectures, integration patterns, and delivery-ready implementation guidance.
• Lead by example through hands-on prototyping, architecture reviews, troubleshooting, code/design reviews, and technical support for delivery teams.
• Ensure security, governance, compliance, and operational readiness are built into solutions from design through production deployment.

Must have

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field.
• 10+ years of IT experience, including 5+ years in senior solution architecture roles for enterprise data, analytics, and AI platforms.
• 5+ years strong hands-on experience with Azure-based data platforms, including Azure Data Factory, Databricks, data lake architectures, SQL, and dimensional data modeling, with the ability to build practical end-to-end solution designs.
• Strong experience with enterprise security, governance, and compliance, and the ability to work effectively with enterprise architects and guiding or regulatory functions to ensure solutions meet organizational standards and control requirements.
• Enterprise AI enablement experience: designing solutions with Azure OpenAI and retrieval-augmented (RAG) patterns.
• BI/analytics architecture experience with Power BI or a similar enterprise BI technology.

Advantages

• Deep hands-on expertise in Power BI architecture, including semantic models, DAX, Power Query, workspace strategy, governance, performance optimization, and enterprise reporting patterns.
• Experience with Microsoft-first AI platforms and services such as Azure AI Foundry, Copilot Studio, Azure AI Search, Semantic Kernel, Prompt Flow, and enterprise AI governance patterns.
• Experience with Microsoft Fabric, Azure DevOps, GitHub Enterprise, and CI/CD for Data or AI solutions.
• Experience with Microsoft security and governance services such as Microsoft Entra, Purview, and Key Vault.
• Experience in life sciences, pharmaceuticals, or other regulated industries.
• Relevant Microsoft certifications in Azure, Power BI, data, or AI architecture.

Key skills

• Hands-on technical leadership with a clear lead-by-example mindset.
• Strong architecture judgment across data, analytics, AI, integration, security, and non-functional requirements.
• Proven ability to own solution architecture end to end: shape target designs, validate them through prototyping or proof of concept where needed, guide delivery teams through implementation, and provide technical oversight through design reviews and production readiness checks.
• Ability to translate business problems into practical, delivery-ready technical solutions.
• Strong troubleshooting, decision-making, estimation, and risk management capability.
• Comfort working across distributed teams and external suppliers in enterprise delivery environments.
• Excellent communication skills in English, with the ability to drive alignment across architects, delivery teams, business stakeholders, and enterprise control functions.

Core technology stack

• Analytics: Power BI, semantic models, DAX, Power Query, Databricks, enterprise reporting and governance.
• Data platform: Azure Data Factory, Databricks, Azure data lake, SQL, dimensional modeling, ETL/ELT, orchestration.
• AI platform: Azure OpenAI, Azure AI Foundry, Azure AI Search, Copilot Studio, Semantic Kernel, Prompt Flow.
• Engineering and governance: Azure DevOps, GitHub Enterprise, CI/CD, Microsoft Entra, Purview, Key Vault.
• Optional / advantageous: Microsoft Fabric, Python, data observability and monitoring patterns.