Senior Data Scientist, Business Analytics

Venice.ai•Sheridan, WY

About The Position

Venice is hiring a Senior Data Scientist to serve as a tactical analytics leader and authoritative source on company data. The role owns data end to end, including where data is sourced and stored, how it flows and is accessed, and how it is queried. The position builds the systems, infrastructure, tools, and analysis needed to support rigorous business and product decisions while working within Venice’s privacy-first approach to collecting less user data. Reporting to the VP of Business Operations, this role partners with product managers, engineering, finance, marketing, and the leadership team. It assesses Venice’s internal data warehouse and product analytics tools, identifies gaps, and develops shared data access, dashboards, self-service analysis, data models, and reporting pipelines. The role supports experiment tracking, consolidates business data, and proactively identifies strategic questions and provides recommendations with clear caveats about uncertainty and data limitations. The Data Scientist also helps instrument products thoughtfully, advises on the tradeoffs between data granularity and user trust, and develops analytical methods suitable for limited data and a rapidly changing product. The role requires product and business judgment alongside engineering depth, strong communication, and an ability to help decision-makers act without overstating what the data can establish. Other duties may be assigned.

Requirements

At least 7 years of demonstrated experience.
Product-minded data leadership and the ability to think like a product manager and business owner, prioritizing better decisions over more dashboards.
Ability to translate business questions into analyses and recommendations, proactively surface important questions, and consider funnels, acquisition sources, and conversion.
Experience working with messy, incomplete, or deliberately constrained data and using approaches such as quasi-experiments, proxy metrics, qualitative triangulation, and decision frameworks.
Ability to work in databases and partner with Engineering to influence robust data systems.
Production-quality coding skills and fluency in SQL.
Ability to build data pipelines, models, and tooling that become infrastructure.
Understanding of data warehousing concepts and experience with modern analytics stacks.
Experience with PostHog for product analytics and Postgres for data storage and querying.
Clear communication, including presenting to executives without jargon and honestly explaining what data does and does not show.
Respect for Venice’s privacy mission and agreement with its approach to collecting less data; willingness to make decisions within that constraint rather than work around it.
Ability to move quickly, iterate on analytical methods and production tooling, advocate for what is needed, and see work through.
Interest in using AI agents and LLMs to accelerate analysis, automate repetitive data work, and surface insights.

Nice To Haves

Familiarity with more than one of Venice’s technology stack languages: Rust, TypeScript, Go, and Python.
Understanding of PostHog’s potential as a central platform across error tracking, support, and conversation data.
Experience or considered opinions regarding the ethics of data collection and what companies should and should not track.
Excitement about using AI agents as part of an analytical workflow.

Responsibilities

Own data end to end, determining where it lives, how it flows, how it is accessed, and how it is queried.
Build data infrastructure, tooling, dashboards, and analysis that establish a reliable source of truth for company data.
Consolidate access to financial statements, product metrics, and growth analysis, including building a shared data lake with specialized views.
Support Product’s experiment tracking by building systems that let teams monitor tests without managing spreadsheets.
Proactively identify and answer strategic questions about the business and products, using appropriate methods for limited data and communicating uncertainty and confidence levels.
Assess the internal data warehouse and product analytics tooling, identify gaps, and build missing dashboards, self-service analysis tools, data models, and reporting pipelines.
Partner with stakeholders across the organization to provide insights that guide and reshape roadmaps.
Work with Engineering to instrument products thoughtfully, collecting what is needed for decisions without unnecessary tracking.
Work with Finance on business metrics, Marketing on growth attribution, and executives on company-level metrics and strategic questions.
Advocate for the privacy line by evaluating proposed tracking, explaining data-collection tradeoffs, and finding analytical methods that do not require invasive tracking.
Develop metrics robust to frequent product changes and help distinguish meaningful trends from noise.