Data Science & Analytics:Have familiarity with designing, developing and deploying statistical models, machine learning algorithms, and analytical framework to support channel business decisions. Have hands on experience in conducting exploratory data analysis to surface insights on sales performance, inventory. Have familiarity with building an maintaining forecasting models. Translate complex analytical outputs in clear, actionable recommendations for business audience. Data Governance & Metric Definition:Serve as the data steward for key data domains owning definitions, lineage and quality standards. Develop an maintain data dictionaries, business glossaries and lineage documentation. Define and monitor data quality riles, triage and resolve data issues in partnership with engineering. Ensure AI outputs and agent generated content are traceable back to governed, trusted data sources. Design, build and deploy AI agents that automate recurring analytical workflow in data governance. What You’ll bring. Requiredexperience 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, Neo 4 j)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, Lang. Graph, Crew. AI, 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, Big. Query, Databricks) Strong proficiency in SQL and Python (e.g., pandas, scikit-learn, statsmodels, or similar)Have familiarity with designing, developing and deploying statistical models, machine learning algorithms, and analytical framework to support channel business decisions.experience in a retail or consumer goods environment. Practical experience building or deploying AI agents or LLM-powered applications. Experience with data governance practices, including data quality, metadata management, or data stewardship. Familiarity with knowledge graph technologies (RDF, property graphs, Neo 4 j) Mandatory Skills: Fullstack Java Enterprise . Experience: 8-10 Years .