Chen Chen Data Science Lead Role Plan — English Edition

Chen Chen: Memova Data Science Lead Role Plan

Purpose of the Material

This is a first-person role pitch presented by Chen Chen, not a third-party talent evaluation. Its purpose is to explain his education and professional background, his understanding of Memova, the responsibilities of a data science lead, and an execution plan spanning the first 90 days through the following 12 months.

Narrative Structure

Pages 1–5 introduce Chen’s background through a timeline: a Stanford PhD in Civil and Environmental Engineering with a Computer Science machine-learning minor, followed by work at Twitter, PayPal, and OpenAI beginning in 2025. The academic section includes three papers in Building and Environment and one paper presented at IBPSA Building Simulation 2021. The professional section emphasizes experimentation, causal inference, prediction, and product decision-making.

Pages 6–7 connect Memova’s company positioning with the mission of the role. Memova turns personal Context into action that can be understood, aligned, and executed. The data science lead is responsible for building a reliable loop from Context to action.

Pages 8–10 present a three-layer data-science framework:

1. Personal Context Intelligence
2. Alignment and Workflow Policy
3. Learning, Evaluation, and Governance

Pages 11–15 map prior experience to the role, define deliverables for days 0–30, 31–60, and 61–90, and outline a platform roadmap for months 6–12.

Page 16 closes in the first person with four decisions that require confirmation from the team.

Key Decisions

• Remove all Meta and Instagram experience, metrics, and associated claims.
• Consolidate five fragmented technical modules into three high-level framework pages centered on understandable Context, decision policy, and a trustworthy learning loop.
• During the first 90 days, prove one real end-to-end use case before attempting to platformize data, models, experimentation, and governance.
• Preserve human authority in automation by setting distinct thresholds for recommendation, drafting, and high-impact execution, with confirmation, undo, audit, and rollback.

Final Artifact

The final deliverable contains 16 pages, primarily in Chinese, with institutional logos used as supporting visual elements. It follows the visual direction of the provided reference PDF and has completed page-by-page visual review, canvas-overflow checks, and file-integrity validation.

For the Company and People chapter, the important evidence is the role design: a data-science function that does not merely optimize model metrics, but connects personal Context, policy, evaluation, governance, and user correction into one accountable product loop.