Job Summary
Required
5+ years of experience across data science, data engineering, or analytics — with at least 2 years in a retail or consumer goods environment
Strong proficiency in SQL and Python (pandas, scikit-learn, statsmodels, or similar)
Familiarity with causal inference methods and experiment design
Familiarity with building and maintaining data pipelines (Airflow, dbt, Spark, or similar)
Practical experience building or deploying AI agents or LLM-powered applications
Familiarity with knowledge graph technologies (RDF, property graphs, Neo4j)
Experience with data governance practices — data quality, metadata management, or data stewardship
Ability to communicate complex findings clearly to non-technical stakeholders
Nice to have:
Experience with multi-agent frameworks (Claude Agent SDK, LangGraph, CrewAI, or similar)
Exposure to ontology design or entity resolution in a retail context
Familiarity with data governance frameworks (DAMA-DMBOK or simila
Graph query languages (SPARQL, Cypher)
Cloud data platform experience (Snowflake, BigQuery, Databricks)
Key Responsibilities
2. To develop and guide the team members in enhancing their technical capabilities and increasing productivity
3. To ensure process compliance in the assigned module| and participate in technical discussions/review as a technical consultant for feasibility study (technical alternatives, best packages, supporting architecture best practices, technical risks, breakdown into components, estimations).
4. To prepare and submit status reports for minimizing exposure and risks on the project or closure of escalations.