Data ScientistWe are looking for a rare blend of analytical depth, data ownership mindset, and modern AI fluency. As a senior data steward & scientist you will sit at the intersection of advance analytics, knowledge representation, data governance and intelligent automation - helping us unlock the full value of our data assets whole ensuring data is trusted, well understood and for purpose. You will not just analyze data - you will govern the definitions that describe it, and deploy AI agents that automate decision on top of it.Key Responsibilities:Data Science & Analytics: Have familiarity with designing, developing and deploying statistical models, machine learning algorithms, and analytical framework to support channel business decisionsHave hands on experience in conducting exploratory data analysis to surface insights on sales performance, inventoryHave familiarity with building an maintaining forecasting modelsTranslate complex analytical outputs in clear, actionable recommendations for business audienceData Governance & Metric Definition: Serve as the data steward for key data domains owning definitions, lineage and quality standardsDevelop an maintain data dictionaries, business glossaries and lineage documentationDefine and monitor data quality riles, triage and resolve data issues in partnership with engineeringEnsure AI outputs and agent generated content are traceable back to governed, trusted data sourcesDesign, build and deploy AI agents that automate recurring analytical workflow in data governanceWhat You'll bring:Required 5+ years of experience across data science, data engineering, or analytics — with at least 2 years in a retail or consumer goods environmentStrong proficiency in SQL and Python (pandas, scikit-learn, statsmodels, or similar)Familiarity with causal inference methods and experiment designFamiliarity with building and maintaining data pipelines (Airflow, dbt, Spark, or similar)Practical experience building or deploying AI agents or LLM-powered applicationsFamiliarity with knowledge graph technologies (RDF, property graphs, Neo4j)Experience with data governance practices — data quality, metadata management, or data stewardshipAbility to communicate complex findings clearly to non-technical stakeholders