Staff Data Scientist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Data Scientist based in United States.

This role will build and own the foundation of a growing Product Intelligence function within a fast-paced technology environment.
You will combine data engineering, product analytics, experimentation, and machine learning to improve how product decisions are made.
The position offers significant autonomy, with responsibility for defining questions, building the infrastructure to answer them, and turning insights into action.
You will partner closely with Product, Engineering, Customer Success, and leadership to understand customer behavior and product performance.
From instrumentation and self-serve dashboards to experimentation frameworks and predictive models, your work will shape strategic priorities and roadmap decisions.
You will also establish scalable processes, analytical standards, and workflows that help create a mature, data-driven organization.
This is a high-impact opportunity for a hands-on data leader who thrives on ambiguity and enjoys building from the ground up.

Accountabilities:
- Build the product data foundation by defining instrumentation requirements, event schemas, data models, and scalable analysis-ready assets in partnership with data engineering.

- Partner closely with Product teams to translate data into actionable recommendations through clear narratives, visualizations, and audience-appropriate data storytelling.

- Build scalable, intuitive, self-service dashboards that enable teams and leaders to independently explore product data and connect product analytics to OKRs and business outcomes.

- Lead deep-dive and exploratory analyses covering funnels, retention, cohorts, feature adoption, customer behavior, and usage patterns to identify opportunities and inform product direction.

- Serve as a bridge between product data and the broader organization, ensuring insights influence cross-functional decisions and strategic outcomes.

- Establish analytical processes, workflows, documentation, and quality standards that improve data accuracy and allow the function to scale effectively.

- Own the product experimentation practice, partnering with Product Managers and Engineering to define hypotheses, metrics, randomization approaches, test duration, power requirements, and analysis plans before launches.

- Establish experimentation standards, templates, and tooling workflows while applying appropriate quasi-experimental methods when traditional A/B testing is not feasible.

- Apply statistical and machine learning techniques to understand and predict customer behavior, including propensity, adoption, churn, retention, segmentation, time-to-value, and driver analysis.

- Productionize models in partnership with data engineering, ensuring outputs reach operational workflows such as product experiences, customer success platforms, CRM systems, and Product or Customer Success processes.

- Monitor model performance over time, including drift, retraining, validation, and retirement of models that no longer provide sufficient business value.

- Advocate for appropriate analytics and data tooling investments while continuously improving the team's ability to work efficiently and independently.

Requirements:

- 8+ years of experience in data science, product analytics, data engineering, or a related discipline within a B2B SaaS or high-growth technology environment.

- Proven experience working in early-stage or low-data-maturity environments where you have built data foundations and scalable analytical capabilities rather than relying solely on established infrastructure.

- Strong proficiency with SQL, Python, Jupyter notebooks, Snowflake, dbt, Sigma, AWS, and modern data modeling and analytics practices.

- Hands-on experience building production-grade dbt models, transformations, pipelines, testing, documentation, orchestrati