Data Scientist

• Industry: pharma
• Location: fully remote
• Rate: we’re open to your suggestions
• Long-term cooperation

Responsibilities

• Build AI-powered insight generation solutions combining data, business knowledge, and advanced analytics.
• Develop semantic data products, knowledge graphs, and RAG-based capabilities to enrich analytical context.
• Create reusable analytical recipes, agent skills, and decision frameworks for recurring business questions.
• Evaluate and optimize AI, ML, and statistical approaches using robust experimentation and performance metrics.
• Ensure insight quality, explainability, consistency, and scalability across brands, markets, and datasets.
• Partner with cross-functional teams to deliver trusted, enterprise-scale analytics and AI solutions.

Key requirements

• 4+ years of experience as a Data Scientist.
• Strong Python skills with hands-on experience in machine learning and generative AI workflows, including writing production-grade code
• Strong experience working with structured data and analytical methods, including data exploration, statistical analysis, feature engineering, hypothesis testing, segmentation, trend and driver analysis, and translating business questions into robust analytical approaches
• Experience applying Generative AI and LLMs to analytical and knowledge-intensive use cases, including prompt engineering, structured outputs, tool/function calling, context engineering and retrieval-augmented generation (RAG)
• Strong analytical mindset with the ability to balance probabilistic AI techniques with deterministic analytical methods, choosing the right approach based on the problem rather than defaulting to an LLM
• Understanding of data semantics and knowledge representation, and experience connecting business concepts, metrics and entities through semantic models, ontologies and/or knowledge graphs
• Experience with AI and analytical evaluation and defining/tracking quality metrics (e.g. RAGAS, DeepEval, Langfuse)
• Familiarity with multi-agent AI frameworks such as LangGraph, LangChain, or Pydantic AI - sufficient to configure, evaluate, and iterate on agentic system designs
• Experience with vector databases (e.g. pgvector, Pinecone, Qdrant, Weaviate)
• Experience with Azure cloud services (Azure Apps, Containers, Storage, AI Search, AI Foundry) and/or AWS equivalents
• Experience with Databricks (Delta Lake, Unity Catalog, MLFlow)
• Strong analytical mindset with a structured, experiment-driven approach to problem solving
• Fluent in English, both written & spoken

Nice to have

• API & MCP development and integration capabilities (e.g. FastAPI, FastMCP)
• Experience applying analytics and AI to Consumer Health, FMCG/CPG, marketing, consumer insights or brand performance use cases
• Experience in building production-grade end to end agentic AI solutions (full stack development experience)
• Experience with Aegra - open-source, self-hosted Agent Protocol server for running AI agents