Data Scientist, Dataset Assessment (Lead)

Reality Labs Supply Chain is standing up an NPI data foundation to support AI-enabled supply chain workstreams (product change management / PRQ qualification, capacity and Capex planning, in-market quality and incident management, and end-of-manufacturing planning). Before we build, we need an honest inventory and quality baseline of the datasets these workstreams depend on. This role owns that assessment.

What you’ll do “ Build the authoritative inventory of datasets supporting NPI and supply chain processes source systems, owners, refresh cadence, lineage, downstream consumers. “ Run structured stakeholder interviews with data owners, OPMs, manufacturing ops, quality, and engineering to capture how each dataset is actually produced and used, not just how it’s documented. “ Score every dataset against the 7-dimension data quality framework [7 dimensions: …], with evidence and a defensible rationale per score. “ Define and document the scoring rubric, interview guide, and intake template so the work is repeatable after the engagement ends. “ Deliver a prioritized remediation backlog: which datasets are fit for AI/automation use today, which need work, and what that work is. “ Hand off a clear findings readout to Supply Chain leadership and to the incoming Data Engineer.

Data Sets, Data Science, Data Sets, Data Science, Change Management, Manufacturing, Incident Management, Supply Chain, Artificial Intelligence (AI), Capital Expenditure (CAPEX), “ 5+ years in data science, data quality, or data governance roles. “ Demonstrated experience building a dataset inventory or running a data quality assessment at enterprise scale. “ Working knowledge of supply chain, manufacturing, or NPI processes enough to interrogate a forecast, capacity, yield, or inventory dataset credibly. “ Strong SQL plus Python (pandas) for profiling and statistical quality checks. “ Excellent stakeholder interviewing and written synthesis skills; comfortable driving conversations with senior XFN partners. “ Able to deliver a scoped assessment in an 8-week window with minimal ramp., “ Prior Meta experience; familiarity with Meta’s data stack (Hive/Presto, Databricks, Scuba, Unidash) and with PLM/MES data (Teamcenter, factory MES). “ Experience with data catalog / metadata management tooling and formal DQ frameworks (DAMA DMBOK or similar). “ Hardware NPI or consumer electronics supply chain background.