Jobs Data Science

About the Company

We are a European IT company with 200+ specialists delivering SaaS platforms, enterprise solutions, and cloud-native products for clients worldwide. We focus on building strong, long-term teams and supporting continuous professional growth.

About the Internship & Project

We are looking for a Trainee Data Scientist to join a project delivered for a UK-based marketing data platform. The client’s platform connects various marketing channels under one roof, helping businesses understand customer retention and acquisition through advanced Single Customer View dashboards and personalization tools. You will contribute to real client projects, focusing on Marketing Mix Modelling (MMM) and causal marketing measurement. We are looking for someone with strong statistical reasoning who is motivated to build real-world data science skills, work with e-commerce datasets, and grow into a specialized role.

Responsibilities

• Prepare, clean, and process real e-commerce and marketing datasets
• Build, validate, and diagnose Marketing Mix Models (MMM) using Python
• Analyze seasonality, promotions, and pricing, as well as model adstock/carryover and saturation
• Calculate incremental revenue and ROAS, build response curves, and develop budget optimization models
• Work closely with the Head of Data Analysis and CTO, actively questioning model outputs to ensure statistical accuracy
• Compare MMM results with experiments and other attribution methods, visualizing findings and eventually explaining technical results in simple client-facing language

Requirements

• Good foundation in Python for data analysis (pandas, NumPy, SciPy, scikit-learn)
• Solid knowledge of statistics and regression
• Understanding of model validation and the clear distinction between correlation and causation
• Up to 3 links to pet projects or an academic project demonstrating your best Data Science skills
• English level: Upper-Intermediate

Why This Internship is Exciting

• Great opportunity to enter a specialized and rapidly growing field of Marketing Mix Modelling (MMM)
• Work with real e-commerce datasets and influence real commercial decisions
• Receive strong mentorship from experienced professionals ready to teach you MMM, e-commerce marketing, and attribution
• Gain exposure to advanced tools like Bayesian MMM, PyMC, Google Meridian, and Meta Robyn
• A clear path from an internship to a Junior role, with the potential to become the company’s internal MMM expert and transition into client-facing responsibilities

We are the creators of a new fintech era!

Our mission is to change this world by making blockchain accessible to everyone in everyday life. WhiteBIT is a global team of over 1,200 professionals united by one mission — to shape the new world order in the Web3 era. Each of our employees is fully engaged in this transformative journey.

We work on our blockchain platform, providing maximum transparency and security for more than 8 million users worldwide. Our breakthrough solutions, incredible speed of adaptation to market challenges, and technological superiority are the strengths that take us beyond ordinary companies. Our official partners include the National Football Team of Ukraine, FC Barcelona, Lifecell, FACEIT and VISA.

Requirements:

We're looking for a DevOps engineer with 4+ years of hands-on experience, at least 2 of them spent building and running data or analytics infrastructure. You've designed systems that move, store, and serve data at scale. Warehouse maintenance is part of your daily routine, and writing Python, Bash, or SQL scripts is second nature. You treat data security as a core responsibility: you protect confidentiality, enforce zero-trust, least-privilege access, and automate everything you touch. Solid IaC skills are a must.

• IaC & CI/CD: Production experience with Terraform, Ansible, and Helm, plus CI/CD systems (GitLab CI, GitHub Actions, Jenkins, or ArgoCD). We expect you to design pipelines, not just use them.
• Containerization: Working knowledge of Docker and Kubernetes, including running data workloads on it (like Spark on Kubernetes, stateful services, operators).
• Cloud: Solid experience with AWS (S3, EC2, EMR, Redshift, Glue, IAM) or an equivalent cloud provider.
• Linux & Networking: Solid Linux administration skills and foundational networking knowledge relevant to distributed systems.
• Analytical Databases: Hands-on operation of ClickHouse and/or the Hadoop ecosystem (HDFS, Hive, YARN), including cluster setup, tuning, and scaling.
• Data Warehouses: Practical experience with Snowflake or Redshift, covering warehouse sizing, workload management, performance tuning, and cost control.
• Large Databases: Experience administering databases at terabyte scale: replication, backup and restore strategies, partitioning, and migrations with minimal downtime.
• SQL & Scripting: Strong SQL skills, including reading execution plans and optimizing heavy queries. Confident in Python and Bash for automation, data parsing, and investigation.
• Data Lakes & Object Storage: Working knowledge of S3-compatible storage, columnar formats, partitioning strategies, and data lifecycle management.
• Orchestration & Streaming: Familiarity with Airflow (or similar orchestrators) and Kafka, and how they fit into data pipelines.

Extra Credit / Big Pluses

• Experience with Tableau, Power BI, Looker, Superset, or similar BI tools, including administration, performance tuning, and data source design.
• Hands-on experience with dbt and analytics engineering practices.
• Experience with open table formats (Apache Iceberg, Delta Lake, Hudi) and query engines like Trino/Presto or Athena.
• FinOps experience in data platforms: managing Snowflake credits, Redshift clusters, and storage costs at scale.
• Spark performance tuning and job optimization.
• Change data capture (CDC) with Debezium or similar tools.
• Data quality tooling (Great Expectations, Soda) integrated into CI/CD.
• Golang or Rust tooling experience.
• Knowledge of data governance and compliance (GDPR, data retention, PII handling).
• Understanding of business processes in the FinTech/Banking/Crypto sector.

Responsibilities:

• Infrastructure Ownership: Own the infrastructure behind our data and analytics platform, from provisioning to daily operations. Keep it reliable, scalable, and cost-efficient as data volumes grow.
• Infrastructure as Code: Provision and configure all data infrastructure with Terraform, Ansible, and Helm. 
• CI/CD Pipelines: Build and maintain delivery pipelines for both infrastructure and data code (ETL jobs, Airflow DAGs, dbt models, schema migrations), with automated testing, safe rollouts, and fast rollback.
• Database & Warehouse Operations: Maintain ClickHouse, Snowflake, Redshift, and вPostgreSQL at multi-terabyte scale, handling replication, backups, upgrades, capacity planning, and disaster recovery.
• Data Lake Storage: Manage S3-compatible data lake storage, including partitioning, file formats, and lifecycle policies.
• Automation & Script Optimization: Refactor and automate Python, Bash, and SQL scripts to cut runtimes and eliminate manual work.
• Performance & Cost: Right-size clusters and warehouses, tune heavy queries, and keep compute and storage spend under control.
• Monitoring & Alerting: Cover data infrastructure with metrics, logs, and alerts for cluster health, pipeline SLAs, and job failures.
• Security & Access Control: Enforce least-privilege access, secrets management, encryption, and auditability for sensitive data.
• BI Infrastructure: Support the infrastructure behind Tableau or similar BI tools.
• Big Data Platforms: Deploy, tune, and troubleshoot Hadoop, Spark, and Kafka clusters in production.
• Collaboration: Work with data engineers, analysts, and developers to turn their workloads into solid infrastructure decisions.
• Security & Access Control: Implement role-based access, secrets management, encryption at rest and in transit, and auditability for sensitive datasets in line with regulatory requirements.

Working terms

Immerse yourself in Crypto & Web3:

• Master cutting-edge technologies and become an expert in the most innovative industry.

Work with the Fintech of the Future:

• Develop your skills in digital finance and shape the global market.

Take Your Professionalism to the Next Level:

• Gain unique experience and be part of global transformations.

Drive Innovations:

• Influence the industry and contribute to groundbreaking solutions.

Join a Strong Team:

• Collaborate with top experts worldwide and grow alongside the best.

Work-Life Balance & Well-being:

• Modern equipment.
• Comfortable working conditions and an inspiring environment to help you thrive.
• 24 calendar days of paid leave.
• Additional days off for national holidays.

With us, you’ll dive into the world of unique blockchain technologies, reshape the crypto landscape, and become an innovator in your field. If you’re ready to take on challenges and join our dynamic team, apply now and start a new chapter in your career!

Let’s Build the Future Together!

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.

Join an exciting journey to build and scale a global Media Data Foundation for a leading global organization based in Europe. This platform brings together paid media, digital analytics and marketing performance data from dozens of advertising and measurement platforms. It unlocks reliable media reporting, marketing ROI measurement and data-driven investment decisions across markets.

As a Middle Data Engineer, you will play a pivotal role in shaping and implementing the platform's architecture. The focus is on hands-on technical execution and collaboration with cross-functional teams.

Your work will turn raw media and marketing data into trusted, harmonised data products used by marketing, media and analytics teams around the world. You will use modern tools to solve complex challenges, working alongside skilled architects, engineers and business stakeholders.

Key Responsibilities:

• Hands-On Development: Build, maintain and optimize data pipelines for ingestion, transformation and delivery of media data (Meta, Google Ads, DV360, TikTok, The Trade Desk, GA4, CM360 and others).
• Create and implement scalable solutions to handle many data sources, frequent API and schema changes, and high volumes of data.
• Data Modeling & Warehousing: Design and maintain efficient, harmonised data models for cross-platform media reporting in Snowflake.
• Lead the move of transformation logic to dbt, including orchestration, testing, documentation and CI/CD.
• Collaboration: Partner with Solution Architects to translate high-level designs into detailed implementation plans.
• Work closely with Technical Product Owners, media agencies and market teams to align data solutions with business needs.
• Platform Development: Enable data readiness for media dashboards (Power BI), marketing mix modelling / ROI analytics and campaign taxonomy governance.
• Implement robust frameworks to monitor data freshness, completeness, quality and pipeline performance, with automated alerting.
• Security & Compliance: Implement robust access controls and data protection across the pipeline and for data shared with internal and external consumers.
• Ensure compliance with data privacy regulations (e.g. GDPR, CCPA) and internal data governance policies (data product tiers, tagging, naming standards).
• Monitor and address potential vulnerabilities, including service account and secret management, ensuring the platform follows security best practices.

Requirements:

• 2+ years of experience showing technical expertise and critical thinking in data engineering.
• Hands-on experience with dbt and strong Python programming skills.
• Proficiency in Snowflake and strong expertise in data modeling are essential.
• Demonstrated experience with marketing, media or advertising data (ad platform APIs, campaign hierarchies, web analytics) is required.
• In-depth understanding of security and governance in Snowflake: RBAC, secure views, data sharing, masking and row access policies, object tagging.
• Ability to design, implement and promote solution patterns and standards for complex, multi-source data platforms.
• Familiarity with multiple cloud platforms (Azure or GCP preferred, with a focus on Azure, including ADF).
• Experience with ETL/ELT and data integration tools (Adverity, Fivetran or similar) and batch orchestration.
• Competence in SQL, relational database design and efficient methods for data retrieval and preparation at scale.
• Proven ability to collect and process raw data at scale, including scripting, REST API integration and SQL querying.
• Experience with BI tools (Power BI preferred) and supporting reporting consumers.
• Experience working in global environments and collaborating with virtual teams and external agencies.
• A Bachelor's or Master's degree in Data Science, Computer Science, Economics or a related discipline.

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

*not applicable for freelancers

Our Customer:

Our customer is a technology company developing data-driven solutions for digital advertising and real-time campaign optimization. The team works with large-scale data and Machine Learning to support automated advertising decisions and improve campaign performance.

Your tasks:

• Develop, improve, and operate ML models used in production advertising systems.
• Work on ML problems related to bidding, bid optimization, and real-time advertising decision-making.
• Develop models for CTR/CVR prediction.
• Optimize campaign performance, budget allocation, and pacing.
• Work on ranking and decisioning models for advertising systems.
• Analyze large-scale advertising data and identify opportunities for optimization.
• Translate business and advertising problems into practical Data Science / ML solutions.
• Evaluate model performance and business impact using relevant advertising KPIs.
• Conduct experiments and A/B testing to validate and improve ML solutions.
• Collaborate with engineering and product teams to deploy and continuously improve production models.

Required experience and skills:

• 5+ years of commercial experience in Data Science / Applied Machine Learning.
• Hands-on experience in AdTech / Programmatic Advertising.
• Direct experience with RTB / real-time bidding systems.
• Commercial experience applying ML/Data Science to advertising optimization.
• Hands-on experience with bidding or bid optimization.
• Experience with CTR/CVR prediction, campaign optimization, ranking, pacing, budget allocation, or advertising decisioning.
• Experience developing, improving, or operating ML models in production.
• Strong Python skills.
• Strong SQL skills and experience working with large-scale datasets.
• Experience translating business problems into ML/Data Science solutions.
• Strong analytical and problem-solving skills.
• Strong English communication skills.

Would be a plus:

• Experience with DSP platforms.
• Experience with bidder logic, bidding algorithms, or bid-price optimization.
• Knowledge of OpenRTB and auction dynamics.
• Experience with real-time decisioning or pre-bid optimization/filtering.
• Experience with user acquisition, retargeting, or performance advertising.
• Experience with large-scale real-time ML.
• Experience with online experimentation / A/B testing.
• Experience with contextual bandits, online learning, or reinforcement learning.

Our team is seeking a Data Solution Architect to further strengthen our capabilities.

Responsibilities

• Work closely with business in identifying solution requirements and key case-studies/scenarios to architect Data solution
• Participate in Data and Big Data initiatives on a company level
• Design Data analytics solutions by utilizing the Data technology stack with Big Data techniques
• Conduct solution architecture review/audit, calculate and present ROI
• Create and present solution architecture documents with deep technical details to customer and implementation teams
• Participate in specific “pre-sale” activities related to available experience and practice in order to prepare technical proposals on customer requests
• Lead implementation of the solutions from establishing project requirements and goals to solution "go-live"
• Maintain a strong understanding of technical solutions and architecture design trends and best practices, stay on cutting edge of Data technologies
• Constantly grow expertise by gathering and monitoring available EPAM project experience and ongoing projects with clients from different business domains to drive further EPAM business in Data field
• Create and follow personal education plan in the technology stack and solution architecture
• Share experience, knowledge and vision with EPAM, colleagues and customer teams. Participate in seminars, meet ups, mentoring, training programs. Prepare white papers and presentations

Requirements

• Strong ‘hands-on’ experience as a Data Architect with design/development background with Java, Scala, or Python
• Architecture experience and practice in Data Management, Data Storage, Data Visualization, Disaster Recovery, Integration, Operation and Security
• Experience with building traditional Cloud Data Warehouses, Data Lakes
• Knowledge of high load and IoT Data Platform architectures and infrastructures
• Wide experience in analysis, design, implementation, deployment as well as troubleshooting and rebuilding distributed Linux based platforms and Big Data solutions on premises and in Cloud
• Strong Cloud experience in at least one of the Cloud providers (AWS, Azure, GCP)
• Solid experience in continuous delivery tools and technologies
• Broad experience with Containers and Resource Management systems: Docker, Mesos, Kubernetes/OpenShift, Yarn
• Able to deliver Data Analytics projects and architecture guidelines
• Strong in research, comparison, and selection of tools / technologies / approaches to be used
• Practical experience in performance tuning and optimization, bottleneck problem analysis
• Strong communication skills, experienced in team coordination skills and solution implementation supervision
• Good in Agile development methodology, Scrum in particular
• Solid skills in business analysis, network/stack architecture, troubleshooting, support
• Fluent English

Our Customer:

Our customer is a technology company developing data-driven solutions for digital advertising and real-time campaign optimization. The team works with large-scale data and Machine Learning to support automated advertising decisions and improve campaign performance.

Your tasks:

• Develop, improve, and operate ML models used in production advertising systems.
• Work on ML problems related to bidding, bid optimization, and real-time advertising decision-making.
• Develop models for CTR/CVR prediction.
• Optimize campaign performance, budget allocation, and pacing.
• Work on ranking and decisioning models for advertising systems.
• Analyze large-scale advertising data and identify opportunities for optimization.
• Translate business and advertising problems into practical Data Science / ML solutions.
• Evaluate model performance and business impact using relevant advertising KPIs.
• Conduct experiments and A/B testing to validate and improve ML solutions.
• Collaborate with engineering and product teams to deploy and continuously improve production models.

Required experience and skills:

• 5+ years of commercial experience in Data Science / Applied Machine Learning.
• Hands-on experience in AdTech / Programmatic Advertising.
• Direct experience with RTB / real-time bidding systems.
• Commercial experience applying ML/Data Science to advertising optimization.
• Hands-on experience with bidding or bid optimization.
• Experience with CTR/CVR prediction, campaign optimization, ranking, pacing, budget allocation, or advertising decisioning.
• Experience developing, improving, or operating ML models in production.
• Strong Python skills.
• Strong SQL skills and experience working with large-scale datasets.
• Experience translating business problems into ML/Data Science solutions.
• Strong analytical and problem-solving skills.
• Strong English communication skills.

Would be a plus:

• Experience with DSP platforms.
• Experience with bidder logic, bidding algorithms, or bid-price optimization.
• Knowledge of OpenRTB and auction dynamics.
• Experience with real-time decisioning or pre-bid optimization/filtering.
• Experience with user acquisition, retargeting, or performance advertising.
• Experience with large-scale real-time ML.
• Experience with online experimentation / A/B testing.
• Experience with contextual bandits, online learning, or reinforcement learning.

Location: Remote, worldwide (except Russia)
Employment: Full-time
Duration: Long-term
Rate: Up to $30/hour
English: B2+/C1
Start: ASAP

About the Role

We are looking for an experienced Senior Azure Data Architect with strong expertise in enterprise data architecture and proven experience designing production-ready data platforms supporting Generative AI (GenAI), Retrieval-Augmented Generation (RAG), and Agentic AI applications.

The ideal candidate has a strong background in Azure data services, particularly Azure Cosmos DB and Azure Synapse Analytics, and has personally designed or taken architectural ownership of enterprise data solutions used by AI-powered applications.

Important: This is a Data Architecture role, not an AI/ML Engineering position. Hands-on development of RAG pipelines or AI agents is not required. The primary focus is designing scalable, reliable, AI-ready enterprise data architectures.

Key Responsibilities

• Design and own enterprise-scale data architectures within the Microsoft Azure ecosystem.
• Architect production-ready data platforms supporting GenAI, RAG, and Agentic AI applications.
• Design data ingestion, transformation, storage, and retrieval architectures for structured and unstructured enterprise data.
• Define AI-ready data architecture patterns, ensuring enterprise knowledge is accessible to AI-powered applications.
• Design and optimize solutions using Azure Cosmos DB, Azure Synapse Analytics, and related Azure data services.
• Develop scalable, metadata-driven and configuration-driven data ingestion frameworks.
• Design data models, lakehouse architectures, and enterprise data integration patterns.
• Lead architectural decisions related to data platform modernization, scalability, reliability, and performance.
• Ensure appropriate data governance, security, accessibility, and maintainability.
• Collaborate with engineering, AI, and business teams to align data architecture with application requirements.

Must-Have Requirements

• Strong expertise in enterprise Azure Data Architecture.
• Deep hands-on architectural experience with Azure Cosmos DB — mandatory.
• Strong experience with Azure Synapse Analytics.
• Proven experience designing production-ready data architectures for GenAI, RAG, or Agentic AI applications.
• Direct involvement in at least one real-world project where you designed or owned the data architecture supporting RAG, GenAI, or AI-agent applications.
• Demonstrated ability to independently design and take ownership of complex enterprise data architectures.
• Experience designing AI-ready data platforms, including data ingestion, preparation, storage, governance, and retrieval.
• Understanding of enterprise knowledge management and data accessibility requirements for AI applications.
• Experience working with structured and unstructured data.
• Strong data modeling, system integration, and architecture modernization skills.
• Experience designing scalable and reliable production data platforms.
• English B2+/C1.

Please note: Theoretical knowledge of RAG or Agentic AI is not sufficient. Candidates must demonstrate practical architectural experience and clearly explain their personal responsibilities and contributions to production projects.

Additional Technical Skills

• Azure Databricks
• Azure SQL Managed Instance / SQL Server / T-SQL
• Azure Data Lake Storage
• Lakehouse Architecture
• Azure Functions
• Metadata-driven and configuration-driven ingestion frameworks
• Enterprise data platform modernization
• Data governance and integration patterns
• Data retrieval and knowledge management architectures

What We Offer

• Fully remote collaboration from anywhere in the world (except Russia).
• Long-term, full-time engagement.
• Competitive hourly rate up to $30.
• Opportunity to work on enterprise-scale Azure data platforms supporting modern GenAI applications.
• Technically challenging projects with significant architectural ownership.
• ASAP start.

Recruitment Process

Interview stages: To be confirmed.

Interested?

Please send your CV along with a brief description of a production project where you personally designed or owned the data architecture for a GenAI, RAG, or Agentic AI application. Highlight your experience with Azure Cosmos DB and Azure Synapse Analytics.

Project Description

A B2B marketing analytics platform for LinkedIn advertisers wants to know whether the early signals of an ad campaign can predict its outcome months ahead. Small B2B advertisers close only a handful of deals per year, so standard attribution has too little data to work with. The proof of concept tests a different approach on the platform's historical data across many advertisers. Engagement and website signals from the first 7-21 days of a campaign are combined into a surrogate index that predicts later pipeline outcomes and states how confident the prediction is.

The PoC is time-boxed to 5-6 weeks and ends with a Go or No-Go decision backed by numbers.

Client Description
A B2B marketing analytics platform for LinkedIn

• A B2B marketing analytics platform for LinkedIn advertisers wants to know whether the early signals of an ad campaign can predict its outcome months ahead. Small B2B advertisers close only a handful of deals per year, so standard attribution has too little data to work with. The proof of concept tests a different approach on the platform's historical data across many advertisers. Engagement and website signals from the first 7-21 days of a campaign are combined into a surrogate index that predicts later pipeline outcomes and states how confident the prediction is.
   

Requirements:

• 5+ years of applied data science or statistics at a senior level.
• Strong command of regression modeling, including regularized and hierarchical (multilevel) models.
• Proven experience with small or sparse datasets and with probability calibration.
• Rigorous validation practice: temporal splits, leakage prevention, overfitting control.
• Python (pandas, scikit-learn, statsmodels) and SQL.
• Ability to explain uncertainty to non-technical stakeholders.
• English - upper-intermediate.

Nice to have

• Bayesian tooling such as PyMC, Stan or bambi.
• Familiarity with surrogate index and proxy metric methods, such as the work of Athey, Chetty, Imbens and Kang.
• B2B marketing analytics: attribution, account-based marketing, LinkedIn Ads, CRM pipeline data.
• Holdout design, controlled experiments and sequential testing.
  Statistical process control.
• Part-time, 0.5-0.8 FTE, about 20-32 hours per week.
  5-6 weeks, starting in October 2026, exact date to be confirmed.
• Possible continuation into productization if the PoC succeeds.

Responsibilities:

• Define, together with the client and a business analyst, what counts as campaign success, the outcome window and the cut-off between signals and outcome.
• Design the analytical dataset: candidate signals, outcome labels, exclusion rules and safeguards against future information leaking into the signals. A Python data engineer builds the dataset in ClickHouse to this design.
• Build the surrogate index as a regularized or hierarchical model, with signal weights shared across advertisers and adjusted per advertiser in proportion to its own data volume.
• Validate the model on campaigns it has not seen: temporal backtesting and leave-one-advertiser-out evaluation, with AUC, Brier score and calibration curves.
• Compare the model against the current practice of judging campaigns by CTR and CPC, and measure how prediction quality changes between day 14 and 21.
• Run error analysis, source ablation and learning curves to show which data is missing for a reliable forecast.
• Present weights, patterns and uncertainty to the team and reccomend Go or No-Go for the future phase.
   

We’re looking for a Senior Data Integration Engineer with strong experience in financial services, banking, or fintech to own and scale client data integrations across our platform.

The ideal candidate combines deep software engineering expertise with hands-on experience integrating complex financial systems, including core banking/processing platforms, digital banking solutions, loan origination systems, and CRM platforms.

JetSoftPro is looking for an experienced Senior Data Integration Engineer to join a project for a US-based fintech company working with credit unions and financial institutions.
Position Requirements:

• 8+ years of software engineering experience, including integration work in financial services, banking, or fintech environments 
• Strong command of modern integration technologies: REST APIs, authentication protocols, and data extraction
• Expert-level SQL, understanding of data warehousing
• Azure Fabric Data Warehouse is a plus
• Experience working directly with clients or external technical stakeholders 
• Strong proficiency with AI tools, understanding AI-native software development process
• Bachelor's degree 

• Responsibilities:
  Own the full lifecycle of client data integrations, from discovery and scoping through implementation, automation, and ongoing operation 
• Design and build integrations with credit union technology systems, including core processing, digital banking, loan origination, and CRM platforms
• Partner with client technical teams and third-party vendors to establish access, define data contracts, and establish new connections 
• Own integration reliability, including monitoring, alerting, and issue resolution 
• Handle sensitive financial data in line with security and compliance standards, including encryption, access controls, and auditability 
• Maintain documentation and repeatable patterns so each new integration builds on the last 
• Evaluate build, buy, and partner options for each integration and recommend an approach 
• Contribute improvements to the broader agentic platform beyond integration-specific work, including serving as a key stakeholder on source-system data, contributing to field definitions, mappings, and domain context
• Actively contribute to the development and maintenance company-wide data warehouse
   

We are looking for a Senior Data Scientist / Python Developer to join an R&D-oriented team working on intelligent solutions for telecommunications and network monitoring.

This is a hybrid role combining strong Python development, Data Science, Machine Learning/AI, statistics, and algorithm design. You will work on solutions for real-time detection and analysis of network and service issues.

What you’ll do:

Develop production-ready solutions using Python

Design and implement ML/AI algorithms for real-time detection of network and service issues

Apply statistics, data analysis, and algorithmic approaches to complex technical problems

Work with Machine Learning and AI technologies, including LLMs

Develop and optimize data-driven detection and monitoring solutions

Contribute to the design of algorithms and data processing approaches

Work closely with an experienced R&D-oriented engineering team

Follow software engineering best practices, including CI/CD, testing, and version control

Work in a Linux environment and contribute to agile development processes

Requirements:

3–5+ years of professional software development experience

Strong Python development skills

Solid background in Data Science / Machine Learning / AI

Experience with statistics and algorithm design

Practical experience with ML/AI, including LLMs

Experience with real-time data processing, detection, monitoring, or anomaly detection is highly relevant

Experience with CI/CD

Good knowledge of Linux

Strong analytical and problem-solving skills

Fluent English — you will work in an international R&D environment

Education

A Master’s degree in one of the following fields is expected:

Computer Science

Physics

Mathematics

Communications Engineering

A PhD is a strong plus, especially for candidates with an R&D or research-oriented background.

Nice to have:

Experience in telecommunications

Knowledge of telecommunications networks

Experience with network monitoring, anomaly detection, or service reliability

Research or R&D experience

Experience working with highly technical engineering teams

What We Offer:

-Paid vacation and sick leave

-We provide the equipment you need to work comfortably (if required)

-We cover 50% of courses if you want to grow professionally

-Regular salary reviews based on your performance

-Competitive salary, depending on your experience

-Always on-time payments — no delays

-A normal, supportive team without unnecessary bureaucracy

About the role
We're looking for a Senior Azure Data Architect for a long-term engagement with an enterprise client. You'll take ownership of an existing Azure data platform, quickly assess and modernise it, and design the data layer behind production GenAI, RAG and Agentic AI applications.
 

This is not a hands-on LLM or AI-agent development role. What matters is that you have designed, in production, how enterprise data and documents are ingested, prepared, stored, governed and made available for retrieval and AI consumption.
 

Must-have
- Recent enterprise Azure data architecture experience with clear architecture ownership
- At least one production project (not a POC) where you designed or owned the data layer for a RAG, GenAI or Agentic AI application: document processing, OCR, chunking, embeddings, vector search
- Azure Cosmos DB in depth: data modelling, partition keys, indexing, RU/cost optimisation, Change Feed
- Azure Synapse Analytics, current experience: Pipelines, Spark, Serverless SQL, Dedicated SQL Pools
- Azure Databricks, ADF, Azure SQL MI / SQL Server / T-SQL, ADLS / Lakehouse, Azure Functions
- Strong data modelling (star schemas, fact and dimension tables), metadata-driven ingestion, structured and unstructured data (PDFs, scans, emails)
- Experience taking over and modernising a legacy data platform
- English C1
 

What you'll do
- Assess an existing Azure data platform, separate quick tactical fixes from the long-term target architecture, and drive both forward
- Deliver implementation-ready architecture: documentation, diagrams and recommendations that engineering teams can act on, with first results within 1-2 weeks
- Design the data and knowledge layer for RAG and Agentic AI applications
- Design scalable metadata/configuration-driven ingestion for databases, APIs and file-based sources
- Make technology-neutral decisions on when Synapse, Databricks, ADF, Azure SQL or a Lakehouse are justified
 

What we offer
- 12-month full-time contract with a long-term perspective
- Start ASAP

We are the creators of a new fintech era!

Our mission is to revolutionize the world by making blockchain technology accessible to everyone in everyday life. WhiteBIT is a global team of more than 1,500 professionals united by a shared vision of shaping the future.

We are building our own blockchain ecosystem to ensure maximum transparency and security for over 8 million users worldwide. Our cutting-edge solutions, rapid adaptation to market challenges, and technological excellence set us apart from traditional companies.

Our official partners include Juventus, FC Barcelona, Lifecell, FACEIT, and VISA.

Join us as an Quantitative Researcher. 

Requirements:

Education & Experience:

— MSc or PhD in a quantitative field; a live trading track record outweighs credentials

— 5+ years in quantitative research or trading at a top-tier HFT firm, market maker, systematic fund, or crypto-native market maker; exchange-side market-structure experience is a plus

— Hands-on experience with at least one TradFi asset class (FX, futures, equities) and / or equivalent crypto asset classes; both maker and taker strategies

Core Technical Skills:

— Advanced probability, statistics, time-series analysis, optimization; strict validation discipline (multiple testing, out-of-sample hygiene, live–sim parity)

— Market microstructure: order-book dynamics, queue position, fill probability, adverse selection and markouts, market impact

— Expert Python (NumPy, pandas/Polars, PyTorch); working C++ or Rust for latency-critical components

— Tick-data engineering at scale: columnar stores, cross-venue normalization, data-quality control

Market Knowledge:

— Crypto microstructure: CEX matching engines and fee schedules, perpetuals (funding, mark price, liquidation/ADL), cross-venue arbitrage, DEX/AMM basics

— TradFi microstructure: FX ECN mechanics (firm vs last-look liquidity, prime brokerage), listed futures; low-latency architecture (tick-to-trade path, colocation, exchange protocols)

— Inventory and hedging: quoting models of the Avellaneda–Stoikov class and extensions, multi-asset inventory optimization, hedging, settlement workflows, real-time risk limits and kill switches

Exchange Microstructure & Mechanism Design:

— Maker-taker economics and market-quality measurement: fee-tier and rebate architecture, per-segment elasticity, spread/depth/slippage/markout metrics, defenses against rebate farming and wash trading

— Liquidity program design: obligation-based schemes (two-sided quoting, presence at touch, minimum depth, uptime) and incentive-based schemes (tiered rebates, credit lines, connectivity benefits)

— Maker protection and flow segmentation: markout-based counterparty scoring, retail price improvement (RPI) order types, asymmetric speed bumps and latency floors, minimum quote life, self-match prevention

— Exchange P&L modeling: net fee capture, subsidy budget and payback, LP unit economics from onboarding to self-sufficiency

Mindset & Soft Skills:

— Owner mentality with direct P&L accountability; ships to production and makes honest kill decisions

— Clear written communication with traders, engineers, exchange management, and external LPs

— Compliance discipline across crypto and TradFi market-abuse regimes

Responsibilities:

Responsibilities — Liquidity Program:

— Design the program end-to-end: MM tiers, admission criteria, obligations (max spread, min depth, uptime, presence at touch), incentives, and graduation to subsidy-free self-sufficiency

— Optimize the fee schedule for net capture: taker tiers priced to measured elasticity by segment; maker rebates funded from taker fees; safeguards against tier gaming

— Design order-book mechanics for comfortable quoting: tick and lot sizing, post-only / iceberg / RPI order types, self-match prevention, minimum quote life, passive-liquidity protection options

— Build the toxic-flow framework: markout-based flow classification, counterparty scoring, continuous monitoring, access rules or surcharges for consistently toxic takers

— Own the program financial model and roadmap: subsidy budget with payback targets; phases bootstrap → organic profitability → self-sufficiency; a market-quality dashboard as the single source of truth

Responsibilities — Trading Desc:

— Own the strategy roadmap — crypto first, then TradFi FX and futures; design pricing, quoting, hedging, and execution as one portfolio

— Run end-to-end alpha research on tick/order-book, cross-venue, funding/basis, and on-chain data

— Design the inventory-aware hedging framework, portfolio risk limits, and parameter governance

— Partner with engineering on low-latency implementation; validate in high-fidelity simulation and canary deployments

— Own P&L attribution, TCA/markout analysis, and research standards

— Lead the TradFi FX entry research workstream (LSEG FX Spot Matching, Euronext FX). 

Working terms

Immerse yourself in Crypto & Web3:Master cutting-edge technologies and become an expert in the most innovative industry.

Work with the Fintech of the Future:

• Develop your skills in digital finance and shape the global market.

Take Your Professionalism to the Next Level:

• Gain unique experience and be part of global transformations.

Drive Innovations:

• Influence the industry and contribute to groundbreaking solutions.

Join a Strong Team:

• Collaborate with top experts worldwide and grow alongside the best.

Work-Life Balance & Well-being:

• Modern equipment.
• Comfortable working conditions, and an inspiring environment to help you thrive.
• 24 calendar days of paid leave.
• Additional days off for national holidays.

With us, you’ll dive into the world of unique blockchain technologies, reshape the crypto landscape, and become an innovator in your field. If you’re ready to take on challenges and join our dynamic team, apply now and start a new chapter in your career!

Let’s Build the Future Together!

The Data/Ingestion Engineer will design and implement scalable ingestion pipelines for high-volume structured and unstructured insurance documents, including PDFs, scans, emails, Word, Excel and PowerPoint files. 

The role includes OCR/document extraction, text cleaning and normalization, semantic chunking, metadata tagging, vector storage/RAG preparation, validation, monitoring and integration with downstream AI solutions.

• At least 7 years of experience as Data Engineer with Python and SQL
• At least 5 with AWS, the rest should be more or less but should be real experience that the candidate is able to talk about and give a concrete example/
• Must to have : 
  • Python
  • SQL
  • AWS
  • S3
  • Step Functions
  • CloudWatch
  • Data/document ingestion pipelines
  • Processing of unstructured documents:
  • PDF
  • scanned documents
  • Word
  • Excel
  • PowerPoint
  • emails
  • OCR / document extraction technologies
  • AWS Textract or equivalent
  • Connectors/integrations with sources such as:
  • SharePoint
  • email
  • Text cleaning and normalization
  • Metadata extraction/tagging
  • Automated error monitoring
  • Validation of OCR/extraction results
  • Git
  • CI/CD
  • Testing
  • Public-cloud data processing/document ingestion experience
  • Good to have :
  • Vector databases
  • RAG
  • Semantic chunking
  • Vector-storage schema design
  • Retrieval mechanisms
  • Azure
  • Databricks
  • AWS Textract specifically
  • Experience within insurance / financial services
  • Experience handling enterprise security and low-latency SLA requirements
• English Level C1
• Duration of the mission :  1 year

Python Developer (Data Integration / Webhooks)

Task: Build a lightweight Python application to ingest third-party webhook data into PostgreSQL for analytics and dashboarding.

Key Responsibilities:

• Develop a Python service to receive and handle incoming webhooks (~22 endpoints/requests).
• Design the database schema and related tables in PostgreSQL.

• Process and store incoming payloads efficiently to support downstream dashboard creation.

   

Requirements:

• Python (FastAPI / Flask / Asyncio)
• PostgreSQL (schema design, SQL, ORM/SQLAlchemy)
• Solid understanding of Webhooks, REST, and JSON payloads
• Docker (containerization & deployment)