Data Scientist

Job Description Data Scientist / AI Engineer

Qualifications

Essential

MSc or PhD in a quantitative field.

5+ years of hands-on experience designing, deploying, and maintaining data science products in complex environments.

Deep applied knowledge across all data lifecycle stages.

Strong understanding of statistics, probability, and machine learning theory.

Proficiency in at least one object-oriented programming language (Python preferred; also Go, Java, C++).

Advanced SQL expertise.

Knowledge of experimental design and statistical analysis.

Customer-centric, value-driven, and execution-focused mindset.

Strong stakeholder management and communication skills.

Continuous learning & improvement attitude.

Desired

Experience with big data platforms (Hadoop, Hive, Spark).

Experience in the energy industry is a plus, but not required.

GenAI & AI Engineering Expectations

Near-Expert-Level Knowledge

Prompt Engineering: few-shot prompting, chain-of-thought, chaining strategies.

RAG (Retrieval-Augmented Generation): including system design, vector search, orchestration.

Embeddings: concepts, generation, usage patterns, vector operations.

High-Level Knowledge of Modern GenAI Concepts

MCP (Model Context Protocol): assets, prompts, tools.

Advanced prompting: step-back prompting, ReAct, prompt optimization, tool-use patterns.

Fine-tuning: when to use/not use fine-tuning, high-level understanding of techniques.