The opportunity
Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades.
What you'll do
Spearhead the next phase of talent intelligence: Take the team beyond descriptive reporting and build the predictive frameworks that show not just what is happening in our workforce, but what will happen next.
Architect a unified workforce data model: Pipeline data across disparate recruiting systems, performance cycles, surveys, and internal tools into a single, cohesive data model that maps the entire employee lifecycle; use AI-native workflows aggressively to parse unstructured text like exit notes and survey feedback at scale.
Define the metrics dictionary and semantic layer: Standardize how metrics like headcount, attrition, and workforce trends are measured across the company; build the semantic layers that keep this data consistent and trustworthy at scale.
Drive data access control and governance: Build the frameworks for role-based access controls and data masking so HR, Finance, and line managers have exactly the access they need without risking sensitive personnel data.
Redesign employee sentiment architecture: Replace traditional annual survey cycles with a continuous, always-on listening framework that captures real-time organizational health and highlights leading risk indicators.
Own product delivery: Act as the technical product owner for internal data interfaces—collaborating directly with Enterprise Engineering to ensure exceptional user utility, predictive accuracy, and data reliability.
What they're looking for
- Enforce rigorous statistical standards: Apply experimental design, A/B testing, and psychometric methodologies to ensure our predictive workforce models and survey frameworks are scientifically backed and statistically valid.
- + years of data science experience, with advanced skills in Python, SQL, and: direct experience with modern data infrastructure like Snowflake, BigQuery, or dbt.
- Strong product focus and experience defining user requirements, collaborating: with engineers, and taking internal data tools from conception to launch.
- Deep understanding of statistics, experimental design, and psychometrics or: survey methodology to design research-backed questions.
