Jobs Data Science

As a member of Computer Vision Team, you will contribute to data preparation, research, prototyping, and the deployment of machine learning models and computer vision algorithms in edge and cloud environments. Your responsibilities will also include monitoring and evaluating solution performance in production and adapting the system to rapidly changing operational conditions.
Required Qualifications:

• 3+ years of experience in computer vision
• High proficiency in Python
• Strong experience with deep learning and the PyTorch framework for computer vision tasks
• Research experience in computer vision with a focus on delivering results
• Ability to work independently or within a small team, with a strong sense of ownership over results

Nice to Have:

• Experience with deep learning solutions for detection, tracking, image retrieval, and segmentation tasks
• Understanding of geometric computer vision principles
• Model optimization: quantization, pruning, neural network compilers
• Hands-on experience implementing low-level CV algorithms
• Knowledge of C++

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.

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 seeking an experienced Data Scientist to take ownership of customer risk-scoring models and exploratory risk analysis. In this role, you will analyze complex behavioral data, conduct deep-dive investigations, and construct statistical models to protect both the business and its users.

Responsibilities

• Design, evaluate, and iteratively refine predictive models targeting AML and Safer Gambling risk indicators.
• Conduct exploratory data analysis and targeted investigations to identify behavioral anomalies and emerging risk patterns.
• Partner with compliance and operational risk teams to translate risk domain insights into practical model features.
• Clearly communicate analytical methodologies, model evaluation results, and strategic insights to non-technical stakeholders.

Requirements

• 5+ years of professional experience in a Data Scientist role.
• Strong technical proficiency in Python and SQL for complex data extraction, feature engineering, and statistical modeling.
• Practical expertise in building and evaluating standard statistical and machine learning models (e.g., XGBoost, Linear/Logistic Regression).
• Strong analytical problem-solving skills with a background in model evaluation metrics and data-driven investigations.
• Effective verbal and written communication skills with the ability to articulate complex analytical concepts clearly.

Nice to have

• Prior experience in online gaming.
• Domain familiarity with Anti-Money Laundering (AML) regulations or Responsible/Safer Gambling frameworks.
• Basic conceptual understanding of model deployment pipelines (hands-on engineering deployment expertise is not required).

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 are looking for a Middle AI/ML Engineer to develop and enhance AI-driven solutions within the Palantir Foundry and AIP ecosystem.

In this role, you will focus on building and iterating on machine learning and LLM-based solutions, integrating them into Foundry workflows to support analytics, automation, and decision-making. You will collaborate closely with data engineers, business analysts, and domain experts to deliver practical, production-ready AI solutions.

Responsibilities:

• Develop and enhance machine learning and AI models to support predictive analytics, classification, forecasting, and AI-assisted workflows.
• Build AI and ML solutions within Palantir Foundry, using Python and existing Foundry pipelines, Ontology objects, and workflows.
• Apply LLMs and NLP techniques (e.g. prompt engineering, fine-tuning, embeddings, retrieval-augmented workflows) using Palantir AIP for enterprise use cases.
• Collaborate with data engineers to understand data sources, ensure data quality, and prepare datasets for model training and inference.
• Conduct experiments, evaluate model performance, and iterate on features and model approaches.
• Integrate AI models into Foundry workflows to surface insights and support business processes.
• Support model deployment and monitoring by following established team standards and best practices.
• Work closely with business and domain stakeholders to translate requirements into practical AI-driven solutions.
• Document model behavior, assumptions, and limitations to support transparency and compliance.
• Stay up to date with applied AI and GenAI trends and contribute ideas under guidance from senior team members

Requirements:

• 3+ years of experience in machine learning, AI engineering, or applied data science.
• Strong Python skills; experience with ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Practical experience with RAG architectures, vector databases, and retrieval strategies
• Hands-on experience with LLMs, NLP, or GenAI use cases (e.g. prompt design, embeddings, text classification, summarization).
• Practical understanding of the ML lifecycle: data preparation, feature engineering, model training, evaluation, and iteration.
• Experience working with structured data (tabular, time series); exposure to text or unstructured data is a plus.
• Familiarity with enterprise data environments and collaborative development workflows.
• Ability to clearly explain model results and AI behavior to non-technical stakeholders.
• Upper-Intermediate English or higher.

Nice to have: 

• Proficiency in Foundry Ontology, Object Builders, and Code Repositories.
• Experience in big pharma or highly regulated industries.
• Knowledge of data privacy, compliance, and security best practices in AI applications.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).

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

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

Project focus:

EKG Access MCP Server.

Must Have:

- Strong Python backend development experience

- Prompt Engineering

- AWS OpenSearch

- Semantic Web technologies: RDF, SPARQL, SHACL, OWL

- Experience with metaphactory or other graph engines

- CDC / event-driven integration

- NER and full-text search

- MCP server implementation

- Experience defining typed tool definitions

- Experience working with YAML / JSON specifications.

Nice to Have:

- Neo4j

- Snowflake

- Snowflake Semantic Model

- OSI-compliant YAML

- End-to-end platform wiring / integration

- Additional MCP experience

- Pharma Commercial experience.

About RiskSeal

RiskSeal is a B2B startup serving fintech clients globally. Our API-first credit risk management platform enables digital banks and consumer lenders to simplify onboarding through data-driven insights.

We are growing rapidly and value collaboration with passionate, creative professionals. Join us to innovate, contribute your expertise, and grow alongside our team.

Headquarters: Dallas, Texas, USA

European Office: Warsaw, Poland

Job Description

We are looking for a motivated Data Scientist to join our team in a fully remote role. The ideal candidate has experience in credit risk modelling, strong Python and SQL skills, as well as hands-on experience with scoring models. You will build and optimize advanced credit risk and anti-fraud models by leveraging machine learning, anomaly detection, and alternative data alongside traditional scoring. Working with client data, you will translate complex algorithmic outputs into clear, actionable insights for clients, partners, and internal teams.

What we're looking for:

• 2+ years of experience in credit risk modelling.
• Solid knowledge of classical methods (logistic regression, decision trees, scorecards) and ML (boosting, random forests, neural networks).
• Strong Python and SQL skills.
• Background in a bank, fintech, or data vendor environment.
• Experience with PD modelling, scoring, and portfolio performance metrics.
• Comfortable working end-to-end: from raw data to a validated, production-ready output.
• Able to present findings clearly to non-technical audiences, including clients and business partners.
• Upper-Intermediate English level or higher.

What you'll be doing:

• Build, test, validate, and implement predictive credit risk, scoring, and anti-fraud models.
• Analyze complex client data and alternative data streams to uncover hidden insights and deliver data-driven recommendations that guide strategic decisions.
• Continuously monitor and audit model performance (tracking drift, Gini, and PSI) to ensure long-term stability and accuracy.
• Design and experiment with new data attributes, leveraging digital footprint and transactional data to improve model predictability.
• Present analytical findings, model performance reports, and innovative ideas directly to clients, partners, and internal teams.
• Conceptualize new analytical products, scorecards, and advanced data solutions to expand RiskSeal's product offering.
• Partner closely with business, product, and tech teams to deploy models and seamlessly turn data insights into scalable, real-world value.

Nice to Have

• Experience working with alternative data sources.
• Knowledge of model governance and validation frameworks.
• Experience presenting analytical solutions directly to clients.
• Exposure to fintech products, digital lending, or embedded finance.

What We Offer

● A fully remote, full-time role in a growing product fintech company
● A fast-paced environment with minimal bureaucracy and quick decision-making
● The opportunity to work on credit risk models, scoring solutions, and real client data
● Direct collaboration with business, product, and tech teams
● The chance to contribute to analytical products that create real value for clients
● Competitive compensation and opportunities for professional growth

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 looking for a Senior AI/ML Engineer to design, develop, and enhance AI-driven solutions within the Palantir Foundry and AIP ecosystem.

In this role, you will take ownership of developing production-ready machine learning and LLM-based solutions, from understanding business needs and exploring data to designing technical approaches, implementing solutions, and integrating them into Foundry workflows. You will work closely with data engineers, business analysts, and domain experts, contribute to technical decisions, and help establish best practices for building scalable and reliable AI solutions.

Key Responsibilities:

• Design and develop AI/ML solutions for predictive analytics, classification, forecasting, automation, and AI-assisted workflows within Palantir Foundry, leveraging Python, Ontology objects, pipelines, and workflows.
• Design, implement, evaluate, and optimize LLM/NLP and RAG solutions using Palantir AIP, including prompt engineering, embeddings, retrieval strategies, vector search, and text processing.
• Own the end-to-end lifecycle of AI solutions, from experimentation and model development to deployment, monitoring, and continuous improvement.
• Collaborate with data engineers to understand data sources, ensure data quality, and design efficient and scalable data processing approaches.
• Integrate AI solutions into production workflows and optimize them for accuracy, reliability, latency, scalability, and cost-efficiency.
• Contribute to technical architecture and design decisions, including model, framework, and tool selection; proactively identify technical risks, improvement opportunities and proactively propose solutions.
• Work with business and domain stakeholders to translate business needs into practical, scalable, and production-ready AI solutions, clearly communicating technical decisions and model limitations.
• Identify technical risks, limitations, and opportunities for improvement and proactively propose solutions.

Requirements:

• 4+ years of experience in machine learning, AI engineering, applied data science, or related fields.
• Strong Python skills and hands-on experience with ML frameworks such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Strong practical experience with LLMs, NLP, or GenAI applications, including prompt engineering, embeddings, text processing, summarization, or similar use cases.
• Solid hands-on experience designing and implementing RAG architectures, vector databases, and retrieval strategies.
• Strong understanding of the ML lifecycle, including data preparation, feature engineering, model development, evaluation, deployment, and monitoring.
• Experience working with both structured and unstructured data, including tabular data, time series, text, and documents.
• Ability to independently analyze ambiguous problems, propose technical approaches, and drive solutions from concept to implementation.
• Strong communication skills, with the ability to explain AI concepts, technical decisions, and model behavior to non-technical stakeholders.
• Upper-Intermediate English or higher.

Nice to Have:

• Proficiency in Foundry Ontology, Object Builders, and Code Repositories.
• Experience in big pharma or highly regulated industries.
• Knowledge of data privacy, compliance, and security best practices in AI applications.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)

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

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