Head of Data & AI

Data Engineering

• Design and evolve a scalable, reliable, and maintainable data platform architecture
• Oversee development of robust ETL/ELT pipelines and real-time data streaming systems
• Establish engineering best practices, including code reviews, CI/CD, data contracts, and observability
• Lead technology selection and resource planning across ClickHouse, Spark, and supporting infrastructure
• Ensure data quality through monitoring, alerting, SLA ownership, and remediation processes
• Manage infrastructure costs and drive optimization across storage, compute, and cloud resources

Artificial Intelligence & Large Language Models

• Define and develop the company’s overall AI direction and roadmap, aligning initiatives with long-term business strategy
• Ensure operational stability and observability of ML services
• Define and drive LLM strategy, including identifying high-value use cases, evaluating model providers (OpenAI, Anthropic, open-source), and leading end-to-end implementation
• Architect and oversee LLM-powered products, including RAG pipelines, AI agents, and intelligent automation workflows integrated into core business processes
• Establish MLOps/LLMOps best practices, including model versioning, evaluation frameworks, prompt management, and drift/hallucination monitoring
• Drive responsible AI governance, including bias detection, explainability (SHAP, LIME), fairness auditing, and compliance with emerging AI regulations
• Evaluate and integrate vector databases (Pinecone, Weaviate, pgvector) and embedding strategies to power semantic search and knowledge retrieval
• Champion AI-assisted development practices (e.g., GitHub Copilot, Cursor) and foster an AI-augmented engineering culture across data teams

Data Analytics & Quant

• Drive advanced analytics, strategy, and Quant development
• Partner with stakeholders to translate complex business challenges into data-driven solutions
• Define and own key metrics, dashboards, and reporting frameworks to support executive and board-level decision-making
• Lead experimentation practices to validate business impact of models and initiatives
• Mentor teams, setting technical standards and career development paths

• 7+ years of hands-on experience across Data Engineering, Data Science, Quant, Data Analytics, delivering end-to-end solutions
• 3+ years of managerial experience, leading data teams
• Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, Engineering, or a related field
• Strong programming skills in Python, with experience writing clean, production-grade code
• Solid understanding of software engineering best practices (CI/CD, testing, code reviews, clean architecture)
• Deep understanding of core ML algorithms: regression, gradient boosting, time series, etc.
• Practical experience with ML libraries and platforms (e.g., scikit-learn, XGBoost, TenserFlow, PyTorch)
• Strong foundation in mathematical statistics, probability theory, and quantitative modeling
• Proficient in SQL and experience with analytical and OLAP databases
• English Upper-Intermediate

• Background in trading or fintech
• Experience analyzing and modeling time series or high-frequency data
• Familiarity with anti-fraud systems, risk modeling, or portfolio analytics
• Practical experience with integration of LLM with corporate systems for internal users

• 20 paid vacation days per year
• 10 paid sick leave days per year
• Public holidays according to current legislation
• Medical insurance
• Opportunity to work remotely
• Professional education budget
• Language learning budget
• Wellness budget (gym membership, sports gear and related expenses)