Data Scientist Lead, Vice President

We are seeking a
Data Scientist Lead
to help build and evolve a high-quality measurement data foundation that enables trusted analytics and decision-making s role focuses on designing and delivering resilient datasets, pipelines, and reusable metrics that support hypothesis-driven analyses and experiments across the product development lifecycle (PDLC).

You’ll be hands-on where needed, drive engineering standards, and help teams move faster by improving reliability, observability, and usability across the data lifecycle-so leaders can clearly see what's driving value, what's creating friction, and what operating-model shifts materially improve outcomes as teams become more agentic.

• Design, build, and operate scalabledata pipelines(batch and/or streaming) with clear SLAs, monitoring, and incident response practices.
• Develop and curate trusted data products(e.g., conformed dimensions, event models, marts) with strong documentation and clear ownership.
• Build and maintain well-defined metrics and feature-ready datasetsthat enable measurement of AI adoption and productivity outcomes (e.g., reusable aggregates, cohorting, time-windowed measures), including change control as definitions evolve.
• Drivedata quality and governance through validations, reconciliations, lineage, access controls, retention, and auditability aligned to requirements.
• Develop and operate workflow orchestration (e.g.,Apache Airflow) to schedule, monitor, and manage data movement and transformations.
• Model and transform data for analytics usingSQL/dbtto support trusted reporting and repeatable measurement.
• Write production-grade
  
  Python/PySparkwith disciplined testing, performance tuning, and maintainable design.
• Partner with analytics, product, and engineering stakeholders to define requirements, success criteria, and consistent interpretation of key measures- particularly where inputs span finance business cases,PDLC/SDLC tools, andAI tool logs.
• Establish and enforce engineering best practices (version control, code review, testing strategy, deployment processes, runbooks) and continuously improve observability and cost/performance (freshness, completeness, timeliness, scalability, spend).
• Mentor and develop a team of2, influencing technical direction through standards, reviews, and knowledge sharing.

• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
• 5+ years of hands-on experience delivering production data solutions in a fast-paced engineering environment (actively coding and owning outcomes).
• Strong software engineering fundamentals (system design, data structures, object-oriented programming, testing strategies, and end-to-end development lifecycle).
• Strong understanding of data modeling (conceptual, logical, physical), including dimensional, normalized, and event-based approaches.
• Hands-on experience with Databricks and large-scaled distributed data processing/performance tuning (Spark/PySpark).
• StrongSQLskills and experience with modern transformation tooling (e.g.,dbt), including building maintainable, testable data codebases.
• Experience designing and operating orchestration pipelines using
  
  Airflow(or equivalent), including backfills, retries, and operational monitoring.
• Demonstrated rigor building and maintaining trusted metrics(definitions, edge cases, validation/testing, documentation) and keeping them reliable as upstream sources change.
• Demonstrated ability to lead delivery in complex environments with multiple stakeholders and ambiguous requirements.

• Experience with modern lakehouse/warehouse patterns and broader cloud data platforms (e.g., Databricks, Snowflake).
• Experience with BI/semantic layers and metrics management practices.
• Exposure to experimentation or hypothesis-driven analytics approaches (e.g., measurement design to support tests, rollouts, and pre/post evaluation); deep causal specialization not required.