Jobs Data Science

We are looking for a highly skilled Senior Data Scientist to join our team.  As a Senior Data Scientist, you will be responsible for designing and implementing advanced data models, analyzing complex business problems, and developing solutions that help optimize sales, marketing, pricing, and overall commercial performance in a leading American publishing house.

The role will contribute to the transition from primarily descriptive analytics toward more advanced, data-driven decision making.

Skills & Experience

• Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, or a related field;
• 5+ years of experience in data science roles;
• Strong experience applying data science and machine learning to business problems, ideally in areas such as sales, marketing, customer behavior, demand forecasting, or pricing optimization;
• Proficient in Python and SQL for data analysis, scripting, and machine learning;
• Proficient in using agentic coding tools like Claude Code, Codex, Cursor
• Experience with SciKit-learn, Pytorch, and similar machine learning frameworks for building and deploying machine learning models;
• Experience working with modern data warehouses/lakehouses like Snowflake, Databricks, BigQuery, Redshift
• Strong analytical and problem-solving skills, with the ability to translate ambiguous business problems into data-driven solutions;
• Ability to work independently and collaborate with business and technical stakeholders;
• Fluent in English and excellent communication skills.

Nice to have: 

• Strong background in data science, with extensive experience in leveraging advanced analytics and machine learning to drive data-driven business decisions.

Responsibilities

• Design, develop, and implement advanced data models and machine learning algorithms to solve complex problems in the publishing domain, like author discovery;
• Analyze sales, marketing, customer, product, and pricing data to identify opportunities and support business decisions;
• Develop data-driven approaches to areas such as demand forecasting, customer behavior, sales optimization, marketing effectiveness, and pricing;
• Work with business stakeholders to understand broad or ambiguous business challenges and translate them into analytical and machine learning solutions;
• Collaborate with cross-functional teams to understand data requirements and deliver actionable insights and recommendations;
• Build and maintain scalable data pipelines using technologies such as Snowflake, and DBT;
• Leverage Python and SQL for data analysis, model development, experimentation, and automation;
• Utilize SciKit-learn, Pytorch and similar frameworks for developing and deploying machine learning models;
• Help evolve the organization from descriptive analytics toward predictive and prescriptive analytics and data-driven decision making;
• Stay current with emerging trends and technologies in data science and machine learning.

We are looking for an experienced Azure Data Architect to join a long-term European project. The project is planned for one year. 

The role focuses on designing and evolving enterprise data architecture in Azure, including the data layer supporting GenAI, RAG, and Agentic AI applications. This is an architecture-focused position — hands-on development of AI agents or RAG solutions is not required.

Responsibilities:

• Design and evolve scalable enterprise data architecture on Microsoft Azure.
• Own architectural decisions related to data modeling, ingestion, storage, integration, and modernization.
• Design data platforms and data layers supporting GenAI, RAG, and Agentic AI applications.
• Define approaches for preparing, storing, governing, and exposing enterprise data and knowledge for AI consumption and retrieval.
• Design solutions for both structured and unstructured data.
• Develop scalable metadata- and configuration-driven data ingestion approaches.
• Design and optimize Data Lake and Lakehouse architectures.
• Define architectural patterns and integrations across Azure data services.
• Ensure scalability, maintainability, security, and reliability of data-platform solutions.

• Collaborate with engineering and other technical teams on architecture implementation.

   

Requirements:

• Strong experience in enterprise Azure Data Architecture.
• Proven experience designing production data architecture supporting GenAI, RAG, or Agentic AI applications.
• Hands-on project experience designing or owning the data layer behind a production RAG/Agentic AI solution.
• Strong knowledge of Azure Cosmos DB — essential.
• Strong experience with Azure Synapse Analytics and Azure Databricks.
• Experience with Azure SQL Managed Instance, SQL Server, and T-SQL.
• Strong knowledge of Azure Data Lake and Lakehouse architecture.
• Experience with Azure Functions.
• Strong knowledge of data modeling, data ingestion, and data-platform modernization.
• Experience working with both structured and unstructured data.
• Experience designing metadata/configuration-driven ingestion and scalable data platforms.
• Understanding of data patterns used in GenAI/RAG solutions, such as document processing, metadata, chunking, embeddings, vector search, and retrieval.
• Experience taking ownership of architectural decisions in complex enterprise environments.

• Spoken English — B2 or higher.

   

We offer:

• Work schedule is flexible — fixed amount of hours that you need to work per month
• 20+ personal days for rest per year (10 days allocated every 6 months; unused days do not expire)
• Up to 12 official public holidays per year (each employee can decide how many to use)
• Birthday day off
• Reimbursement of 5 sick days per year
• Partial compensation for external courses/conferences (after the completion of the Adaptation Period)
• Partial compensation for external professional certifications
• Reimbursement for sports or massage
• Large library with a scheduled purchase of new books every half a year
• Yearly Individual Development Plan (after the completion of the Adaptation Period)

If your experience matches this role, we’d love to hear from you. Apply now!

Build the core data engineering capability that enables WPP Open applications, AI agents, analytics products, and business workflows to securely discover, access, combine, govern, and activate data across enterprise systems, client environments, media platforms, and third-party providers.

We are seeking four senior engineering specialists who will be responsible for implementation and delivery of key platform capabilities. While architecture leadership is provided by the platform architecture team, these hires are expected to bring deep expertise, contribute to solution design, challenge assumptions, and guide implementation decisions based on industry experience.

Requirements

Implement the capabilities required to acquire, standardize, harmonize, and operationalize external marketing, media, commerce, and third-party datasets at scale. 

• Strong experience with SQL, Python, and cloud-based platforms like AWS, GCP, or Azure.
• Solid understanding of data modelling, pipelines and orchestration tools (Airflow, dbt, or similar).
• Familiarity with modern data ecosystems — e.g. Spark, Databricks, Snowflake, BigQuery or Redshift.
• Experience working with CI/CD, version control and testing best practices.
• A proactive approach to solving problems and improving systems.
• Excellent communication skills — you can translate technical concepts into language anyone can understand.
• A passion for data and how it can drive better decisions in media, marketing and beyond.

Experience with technologies and platforms such as

• Adverity, Fivetran, Airbyte
• Google Ads, DV360, Campaign Manager 360, Search Ads 360
• Meta, TikTok, Amazon Advertising, LinkedIn, Pinterest, Trade Desk
• Marketing, advertising, commerce, audience, or measurement data ecosystems

Job responsibilities

• Building and maintaining external data integrations
• Implementing reusable ingestion frameworks
• Standardizing and harmonizing media and marketing datasets
• Integrating third-party and client-owned data sources
• Supporting real-time and batch data acquisition patterns
• Providing expert input into data acquisition and onboarding approaches

About Digis
Digis is a European IT company with 250+ specialists delivering complex products for 70+ clients across the U.S., Europe, and Canada. We operate in a remote-first environment and focus on long-term collaboration, stable projects, and transparent processes.

About the Project
An AI-driven real-time market intelligence platform for the global fresh produce industry. The platform helps growers, retailers, distributors, and investors make faster, data-driven decisions.

Requirements

• 5+ years of professional experience in Data Science.
• Strong Python skills and experience with ML libraries.
• Strong SQL skills and hands-on experience with BigQuery.
• Strong experience with GCP.
• Experience with advanced machine learning frameworks and libraries such as XGBoost, LightGBM, PyTorch, or TensorFlow.
• Upper-Intermediate+ English level

Responsibilities

• Analyze complex multivariable datasets and identify key patterns and drivers.
• Build forecasting and classification models using statistical, ML, and deep learning approaches.
• Engineer features based on seasonality, weather, trade flows, and regional dynamics.
• Design and maintain model evaluation and backtesting frameworks.
• Assess model uncertainty, explainability, and performance.
• Monitor model drift and continuously improve model quality.
• Prototype LLM and AI-agent use cases.
• Collaborate with engineering teams to deploy models into production.

Advantages

• High ownership of the scientific and modeling layer.
• Work with unique global weather, market, trade, and supply-chain datasets.
• Build models that directly impact customer-facing products.
• Direct collaboration with CTO, founders, and senior leadership.
• Freedom to experiment with new approaches and technologies.
• Remote-first working environment.

We Offer

• 20 paid vacation days per year
• 5 paid sick days
• Compensation for professional courses
• English learning support
• Work equipment
• Transparent communication and processes
• Opportunity to switch projects within Digis
• Corporate psychologist

ORIL is looking for a Data Integration Specialist to lead and manage the data integration architecture of our AI-driven facilities management platform. The ideal candidate will possess strong expertise in optimizing data pipelines, resolving intricate integration issues, and ensuring uncompromising data quality.

About the Project

A platform shaping the future of facilities management by connecting buildings, occupants, service providers, and operatives. It streamlines soft FM services so property managers and service providers can plan, source, and manage their portfolios more efficiently and at lower cost, delivering real-time transparency across every site. The ecosystem spans FM service workflows, IoT-connected building sensors, third-party FM platforms, mobile and web clients, and event-driven microservices.
 

What You'll Do

• Design, build, and maintain scalable data pipelines and integrations across internal microservices, third-party FM platforms, IoT sensors, and external APIs.
• Design and implement ETL/ELT pipelines on Azure Databricks, building on Delta Lake and Lakehouse architecture to ingest, model, transform, and serve structured and semi-structured data.
• Define and own the data modelling layer - schemas and domain models that keep data structured and validated before ingestion, avoiding raw data sprawl.
• Work with event-driven architectures (message queues, pub/sub, event streams) to enable real-time data propagation and workflow automation.
• Define and enforce data mapping, transformation rules, and validation standards across all data flows.
• Collaborate with backend engineers, product managers, and data stakeholders to translate integration requirements into robust solutions.
• Monitor integration health, set up alerting, and diagnose pipeline failures to maintain high availability and data fidelity.
• Document integration patterns, data contracts, and API specifications for the wider engineering team.
• Contribute to platform-wide data quality, observability, and reliability standards.

What We're Looking For

• 4+ years of professional experience in a data integration, data engineering, or backend integration role.
• Hands-on experience with Azure Databricks for data engineering, pipeline orchestration, and large-scale transformations.
• Strong proficiency in Python for data engineering, pipeline development, and transformation logic.
• Strong understanding of data modelling, schema design, and data transformation patterns.
• Experience with relational and/or NoSQL databases (PostgreSQL, Cosmos DB, or similar).
• Understanding of distributed systems, microservices architectures, and cloud-native environments.
• High attention to detail and a strong sense of ownership over data quality and integration reliability.
• Comfort working across engineering, product, and business teams.
• English: Upper-Intermediate+

Nice to Have

• Experience with Azure Service Bus or similar event-driven messaging platforms (pub/sub, message queues, async workflows).
• Exposure to real-time streaming data and sensor telemetry workflows.
• Knowledge of data governance, lineage tracking, or observability tooling.

We offer:

• Competitive compensation;
• Accounting support services;
• Flexibility to work from anywhere (fully remotely or in our office in Lviv);
• Comprehensive day-off policy, including public holidays and special events;
• Education & certification budget + corporate education events;
• Wellness budget;
• An agile environment and culture that promotes personal and team development.

Sii Ukraine is a subsidiary of Sii Poland — the leader in IT, engineering, and consultancy services with over 7500 IT experts in Poland and more than 250 prestigious customers. We are looking for ambitious and top-quality professionals to join our project teams.

We are looking for a Senior Big Data Developer to join a project focused on building data-driven solutions and integrations using the Databricks platform.

You will work on backend logic, API integrations and application components within Databricks Apps, contributing to scalable and maintainable solutions that integrate with enterprise systems and procurement processes.

Responsibilities:

• Develop and maintain applications and data solutions using Databricks
• Build and configure Databricks Apps, Workflows, Delta Lake and Unity Catalog
• Develop backend logic using Python, including routing, validation and business logic
• Design and implement integrations with external systems through REST APIs
• Work with JSON, OAuth 2.0 and API-based content routing
• Develop application interfaces using Streamlit or React within Databricks Apps
• Implement and maintain data and API integration flows
• Write unit and integration tests to ensure solution quality and reliability
• Use Git and CI/CD practices for development and deployment
• Implement logging, monitoring and troubleshooting mechanisms
• Collaborate with other developers, architects and business stakeholders

• Contribute to technical decisions and continuous improvement of the solution

   

Requirements:

• Strong commercial experience as a Big Data / Data / Software Developer
• Hands-on experience with Databricks
• Practical knowledge of:
  • Databricks Apps
  • Databricks Workflows
  • Delta Lake
  • Unity Catalog
• Strong Python skills, particularly for backend development
• Experience with REST APIs, JSON and OAuth 2.0
• Understanding of API-based content routing
• Experience with Streamlit or React
• Good understanding of software engineering practices
• Experience with Git and CI/CD
• Experience writing unit and integration tests
• Experience with logging, monitoring and troubleshooting
• Ability to work independently and communicate effectively with technical and business stakeholders
• English at a level sufficient for working in an international environment
• Tax residence in Ukraine required

Nice to Have:

• Experience with E-Invoicing solutions
• Knowledge of SAP Ariba
• Experience integrating with ERP or procurement systems
• Knowledge of UBL / PEPPOL
• Experience working with enterprise integration platforms

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

Responsibilities:
 

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

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

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

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

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

- Implement model output decoding and post-processing;

- Develop camera-related software;

- Write efficient multi-threaded code.

Requirements:
 

- Strong C++ skills;

- Solid background in computer vision;

- Practical experience with deep learning for CV and PyTorch;

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

-Experience with multi-threaded programming and camera systems.

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

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

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

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

What you will work on:

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

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

   

What we expect from you:

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

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

   

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

You should also have:

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

What We Offer:

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

Team Summary

We are a Data Collection team focused on improving smart home devices and features through high-quality data. The data we collect supports machine learning training, evaluation, testing, and performance benchmarking, helping teams develop new capabilities and improve detection quality for customers. We recreate real-world scenarios and edge cases in various indoor and outdoor environments using both production and pre-release devices. We also perform comparative analysis of device motion detection performance across different generations, generating insights that help determine device readiness for mass production.

Job Summary

We are seeking an adaptable and innovative Data Collection Engineer to oversee field operations, akin to debugging and shooting on set. This position is perfect for a hands-on problem solver who excels in dynamic environments and is enthusiastic about working directly with devices at various stages of development. The ability to maintain resilience in unpredictable field conditions is a crucial skill, as you will collaborate closely with cross-functional teams and third-party vendors to enhance performance and detection quality.

Responsibilities and Duties

• Conduct end-to-end data collection using test devices across predefined scenarios and custom action setups
• Independently prepare field locations, including device placement and field-of-view optimization based on environmental conditions
• Perform immediate on-site technical troubleshooting, partnering with Quality Support Engineer when required
• Coordinate third-party providers (vehicles, locations, actors, pets) to ensure correct execution of scenarios
• Mitigate on-site operational risks and align with Project Coordinator on resource constraints

• Document results, issues, and field observations, and drive improvements in data collection efficiency and automation

   

Qualifications and Skills

• Hands-on experience with technical equipment, tools, hardware installations, or field operations. Experience with smart home, home automation or security systems would be a big advantage
• Good understanding of network setup, Wi-Fi configuration, and basic troubleshooting (routers, access points, local networks)
• Strong teamwork, cross-functional collaboration, and high adaptability to changing environments
• Clear and effective communication
• Excellent time management, self-organization, and multitasking skills
• At least A2-B1 level of spoken English

• A confident PC user with robust skills in Google Suite.

   

Nice to have

• Quality Assurance experience (hardware or software testing)
• Working knowledge of UNIX/Linux environments and command-line basics

• Basic scripting skills (bash, python)

   

We offer multiple benefits that include

• The environment of equal opportunities, transparent and value-based corporate culture and an individual approach to each team member.
• Competitive compensation and perks. Annual performance review.
• Opportunity to receive a Performance Bonus for your individual achievements and a Loyalty Bonus recognizing your dedication to the company.
• Gig-contract.
• 21 paid vacation days per year, paid public holidays according to the Ukrainian legislation.
• Development opportunities like corporate courses, knowledge hubs, and free English classes as well as educational leaves.
• Medical insurance is provided from day one. Sick leaves and medical leaves are available.
• Free meals, fruits, and snacks when working in the office.

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

Responsibilities:

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

Requirements:

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

Will be a plus:

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

What we offer:

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

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

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

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

CUSTOMER

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

PROJECT

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

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

Job Description

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

Qualifications

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

WILL BE A PLUS

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

PERSONAL PROFILE

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

Шукаємо уважного та системного фахівця, який допомагатиме розвивати культуру роботи з даними в компанії: забезпечувати їхню якість, прозорість, коректність та зрозумілість для бізнесу й технічних команд.

У цій ролі ви працюватимете з корпоративними даними, документацією, правилами їхнього використання та процесами інтеграції між системами.

 Основні обов’язки:
- Підтримувати та актуалізувати Data Catalog / Data Dictionary: описувати структуру даних, значення полів, джерела, власників і правила використання.
- Проводити моніторинг якості даних: виявляти дублікати, пропущені значення, помилки форматів, невідповідності та інші data quality issues.
- Брати участь у створенні, впровадженні й оновленні політик, стандартів та процедур Data Governance.
- Аналізувати джерела даних, визначати master data, reference data та формувати golden records.
- Контролювати та документувати data lineage — походження даних, їхні трансформації та рух між системами.
- Документувати зміни у моделях даних, бізнес-правилах, мапінгах та інтеграційних процесах.
- Перевіряти та валідовувати нові джерела даних перед їхньою інтеграцією до Data Warehouse / Data Lake.
- Брати участь у процесі Data Issue Management: реєструвати, пріоритизувати, ескалувати проблеми з даними та відстежувати їхнє вирішення.
- Взаємодіяти з бізнес-командами, аналітиками, розробниками та власниками даних для забезпечення єдиних стандартів роботи з даними.
- Використовувати BI-інструменти для перевірки, аналізу та валідації даних.

### Наші очікування:
- Досвід роботи з базами даних та SQL.
- Розуміння принципів Data Governance, Data Quality, Metadata Management.
- Знання типів корпоративних даних: master data, transactional data, reference data.
- Розуміння принципів інтеграції даних між різними системами та джерелами.
- Досвід роботи з Data Warehouse, Data Lake або схожими середовищами буде перевагою.
- Досвід роботи з BigQuery, Snowflake або іншими сучасними платформами для роботи з даними буде плюсом.
- Практичний досвід або розуміння систем Master Data Management (MDM): Tibco, IBM Data Catalog, SAP MDG, Pimcore, Ataccama чи аналогів.
- Досвід роботи з ERP-системами буде перевагою.
- Навички роботи з BI-інструментами — Power BI, Tableau, Looker — на рівні перевірки та аналізу даних.
- Вміння вести структуровану документацію у Confluence, Notion або аналогічних інструментах.
- Аналітичне мислення, уважність до деталей, системний підхід і хороші комунікаційні навички.

 Буде перевагою:
- Досвід участі у впровадженні Data Governance-процесів.
- Досвід побудови або підтримки data catalog, data lineage чи data quality framework.
- Розуміння процесів MDM та принципів формування golden record.
- Досвід взаємодії з технічними та бізнес-командами у межах data-проєктів.

Якщо хочеш, цю вакансію можна назвати ще так: Data Governance Specialist, Data Quality Specialist, Data Steward або MDM Specialist — залежно від того, чи головний фокус у вас на якості даних, документації чи master data.

ПРО НАС: 

SoftServe - це провідна ІТ-компанія, що займається консалтингом та надає послуги у сфері цифрових технологій. Ми забезпечуємо інноваційність, якість і швидкість, яких очікують наші клієнти та їх користувачі. 

AI and Data Science Center of Excellence (CoE) - це головний AI/ML-хаб SoftServe, зосереджений переважно в Європі. У команді понад 170 експертів: Data Scientists, Research Analysts, MLOps Engineers, ML та LLM Architects, які покривають увесь життєвий цикл AI-рішень — від формулювання задачі до продакшн-деплойменту. 

У 2024 році ми реалізували понад 150 AI-проєктів, з яких більше 100 були сфокусовані на Generative AI, поєднуючи масштаб із вимірюваним бізнес‑ефектом. 
Ми є NVIDIA Service Delivery Partner (2024) та активно співпрацюємо з Google Cloud, Amazon і Microsoft, що дозволяє нашим командам працювати з найсучаснішими технологіями. 

Також ми розвиваємо Gen AI Lab — внутрішній інноваційний майданчик для прикладних досліджень та міждисциплінарної співпраці. У 2025 році один з ключових напрямів — Agentic AI: проєктування автономних і колаборативних агентних систем для вирішення складних реальних задач клієнтів і внутрішніх ініціатив. 
 
ЯКЩО ТИ: 

• Senior Computer Vision Engineer та маєш 4+ роки досвіду у computer vision, deep learning та сучасних AI‑системах; 
• добре володієш класичним computer vision, зокрема geometric methods: camera calibration, feature matching, homography estimation та суміжними підходами; 
• маєш значний hands‑on досвід роботи з Python і PyTorch, маєш досвід побудови та підтримки end‑to‑end training pipelines для research та production середовищ; 
• маєш практичний досвід розробки multimodal та generative AI‑систем, включно з transformers, diffusion models та vision‑language models; 
• почуваєшся впевнено, працюючи з моделями на всіх етапах - від експериментів та оцінювання до оптимізації й деплойменту з використанням ONNX, TensorRT або Triton; 
• спеціалізуєшся щонайменше в одному з напрямів: 3D computer vision, anomaly detection, edge AI або model compression; 
• працюєш з cloud‑середовищами та MLOps‑практиками в AWS, GCP або Azure; 
• маєш аналітичне мислення та освіту в галузі Computer Science, Applied Mathematics, Physics або суміжній; 

• володієш англійською мовою на рівні Upper‑Intermediate або вище. 

   

І ТИ ХОЧЕШ: 

• розробляти advanced computer vision та generative AI‑рішення для реальних бізнес‑задач; 
• будувати, fine‑tune, оцінювати та деплоїти моделі для генерації, покращення та розуміння зображень і відео 
•  працювати з великими датасетами та cloud‑інфраструктурою для побудови стабільних production‑ready AI‑систем; 
• співпрацювати з engineers, researchers та stakeholders, перетворюючи ідеї й вимоги на масштабовані технічні рішення; 
• впливати на технічний напрям, ділитися експертизою з командою та драйвити високоефективні AI‑ініціативи; 

• підтримувати тісну співпрацю між Computer Vision Engineers, Data Scientists, DevOps та ML Engineers, створюючи інфраструктуру, ефективні процеси й production‑pipeline’и та інтегруючи їх у існуючі бізнес‑середовища. 

   

РАЗОМ МИ БУДЕМО: 

• розвивати інновації у сфері Computer Vision та Generative AI, працюючи над передовими проєктами з image generation, enhancement, multimodal intelligence та visual understanding; 
• комунікувати й співпрацювати з компаніями світового рівня з нашого logos‑портфеля; 
• використовувати найсучасніші інструменти та технології в межах різноманітних high‑impact проєктів;  
• брати участь у міжнародних подіях та отримувати сертифікації з передових і emerging‑технологій; 
• мати доступ до потужних освітніх і mentorship‑програм;  
• трансформувати software‑індустрію та стимулювати розвиток інтелектуальних візуальних систем, поєднуючи міждисциплінарну експертизу 

Вітаємо в King Group — місці, де зустрічаються найкращі люди з IT- та гемблінг-індустрії, щоб разом створювати масштабні digital-продукти. Ми розвиваємо численні проєкти у сфері iGaming на ринках України та Tier 1, інвестуємо у венчурні стартапи, перспективні ідеї та талановитих людей.

Шукаємо Middle Data Scientist до нашої команди в офіс у Києві. Якщо вам цікаво працювати з великими обсягами даних, будувати ML-моделі та створювати рішення, які впливають на продукт і бізнес — будемо раді познайомитися.

Основні задачі:
 

• Побудова, навчання та валідація ML-моделей (класифікація, регресія, кластеризація) для вирішення бізнес-задач компанії.
• Підготовка, очищення та трансформація даних для навчання моделей машинного навчання.
• Проведення дослідницького аналізу даних (EDA), пошук закономірностей та формування гіпотез.
• Аналіз ефективності моделей, оцінка їх якості за допомогою релевантних метрик та інтерпретація результатів.
• Підтримка, моніторинг та вдосконалення моделей, що працюють у production.
• Оптимізація існуючих моделей та участь у впровадженні нових ML-рішень.
• Співпраця з командами аналітики, розробки та бізнесу для реалізації data-driven рішень.

Основні вимоги:
 

• Ступінь бакалавра/магістра (математика, статистика, інформатика чи суміжна галузь) або еквівалентний практичний досвід, підкріплений хорошими знаннями математики, статистики та теорії ймовірностей.
• Від 2 років досвіду роботи на аналогічній посаді.
• Практичний досвід програмування на Python (Pandas, NumPy, SciPy, Scikit-learn) та впевнене володіння SQL.
• Розуміння класичних алгоритмів машинного навчання: Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Clustering та інших.
• Досвід побудови, навчання, оцінювання та валідації моделей машинного навчання.
• Розвинене аналітичне мислення, відповідальність, самостійність та орієнтація на результат.

Буде перевагою:
 

• Досвід роботи з моделями часових рядів (Time Series), Google Cloud Platform (BigQuery, Cloud Functions), Kedro, TensorFlow або PyTorch, а також з деплоєм та підтримкою моделей у production.

Ми пропонуємо Boost-програми — систему компенсацій для розвитку та комфорту:

• Skill Boost — оплата навчання та курсів.
• Sport Boost — покриття витрат на спорт або абонемент до спортзалу.
• Yummy Boost — оплата корпоративних обідів.
• Content Boost — доступ до розважальних медіапідписок.
• Relocation Boost — підтримка релокації до Києва.

Також:

• Офіційне працевлаштування, медичне страхування, корпоративний лікар.
• Оплачувані лікарняні та відпустка.
• Сучасний офіс у центрі Києва (пішки від м. Либідська/Звіринецька) з панорамною терасою.
• Реферальна програма з бонусами за успішно найнятих кандидатів.
• Команда, яка цінує ініціативність, нові ідеї та прагнення до професійного розвитку.
• Робота над продуктом, де результат моделей напряму впливає на бізнес.
  
   

Зацікавило? Надсилай своє резюме — і познайомимось! 

About the Role
We are seeking an AI & Data Science Consultant to drive the discovery, strategy, and end-to-end delivery of advanced AI initiatives for EPAM’s global clients. This role is a unique hybrid, blending strategic business consulting with deep practical engineering. You will collaborate directly with client executives and EPAM’s delivery teams to transform complex business challenges into scalable, production-ready AI, Machine Learning, and Agentic solutions.
 

Key Responsibilities

• Consult & Strategize: Lead client-facing discovery sessions and workshops to identify high-value AI opportunities, define MVP scopes, and map out strategic delivery roadmaps.
• Design & Architect: Shape scalable AI products incorporating Generative AI, RAG, multi-agent frameworks, semantic layers, and advanced analytics.
• Bridge the Collaboration: Serve as the central link connecting business stakeholders, data scientists, ML engineers, DevOps, and product teams.
• Drive Delivery & MLOps: Apply a strong engineering mindset to ensure solutions are secure, production-ready, and smoothly integrated into cloud infrastructures.
• Support Business Growth: Partner on pre-sales activities, solution estimation, and proposal development while contributing to EPAM’s internal AI and MLOps offerings.
   

Requirements

• Professional Experience: 3+ years of hands-on experience in Data Science, ML, or Applied AI, with a proven track record of bringing models from concept to production.
• Consulting & Communication: Strong active listening, presentation, and storytelling skills; ability to confidently translate complex AI concepts for C-level stakeholders.
• AI & Agentic Tech Stack: Experience with RAG, LLM applications, multi-agent orchestration, and tools like LangChain, LangGraph, CrewAI, LlamaIndex, or Vector DBs.
• Data & Architecture: Understanding of Semantic Layers, metadata, knowledge graphs, and data platforms (such as Databricks or Snowflake).
• Engineering & MLOps: Familiarity with the production model lifecycle, MLOps/LLMOps pipelines, and cloud platform integrations.
   

Nice to Have

• Experience mentoring cross-functional AI teams or driving enterprise-scale GenAI adoption.
• Practical knowledge of cloud infrastructure and DevOps tooling.
• Insights into AI governance and ethical AI frameworks.

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