Staff Data Scientist

Decagon•San Francisco, CA

20d•$250,000 - $300,000•Onsite

About The Position

As a Data Scientist at Decagon, you’ll bring deep technical rigor to how data is analyzed, tested, and applied across the business. You’ll design and interpret experiments, apply advanced statistical methods, and build scalable analytical frameworks that directly inform product strategy and company performance. You’ll work across Engineering and Product to establish robust measurement practices, expand the use of data-driven decision making, and uncover insights that shape Decagon’s trajectory. This role blends strong analytical expertise with product acumen and the ability to influence at the highest levels.

Requirements

Strong ability to structure ambiguous problems, translate them into analytical approaches, and deliver business impact through data.
Advanced proficiency in SQL and Python, with a solid foundation in statistics and experimental design.
5+ years (or equivalent experience) in data science or product analytics roles, ideally within high-scale consumer or marketplace products.
Ability to reason about and analyze machine learning models, understanding their assumptions, trade-offs, and impact on product and customer outcomes.
A proven track record of designing, running, and analyzing experiments at scale.
Excellent communication skills, with experience influencing stakeholders and driving decisions across teams.

Responsibilities

Conduct deep-dive analyses using advanced statistical methods on user behavior, product performance, and business outcomes to surface actionable insights.
Enhance and extend Decagon’s data models, metrics, and dashboards to ensure consistent, scalable reporting across teams.
Design, implement, and analyze controlled A/B test experiments to measure product changes and customer experience.
Collaborate with Product and Engineering to embed experimentation and measurement best practices into product development cycles.
Build and continuously improve Decagon’s data stack, ensuring scalable models, pipelines, and tools that empower self-serve analytics across the company.
Communicate findings clearly and persuasively to executives and cross-functional stakeholders, influencing strategic decision-making.
Evaluate and interpret the performance of ML models (e.g., classification, recommendation, LLM-based systems), helping the team reason through trade-offs, limitations, and business implications of model-driven decisions.
Be the primary driver of experimentation culture at Decagon, helping define how we design, measure, and scale experiments across the business.