AI Data Scientist

About the role

Gamma seeks a Data Scientist to analyze user data and AI outputs, uncovering patterns that guide product decisions for millions of creators. The role measures what matters across more than one million AI-generated presentations and five million AI images created daily. You will help the team ship better features faster by turning data into actionable insights.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Large-scale A/B tests are designed to measure product impact for AI tools on Gamma's platform. Experiments across user cohorts reveal how product changes influence behavior and guide prioritization.

Frameworks that reveal model performance are built so product teams can explore data and run analyses independently. Accessible data structures and tools support evidence-based decisions for product teams.

Investigation of edge cases in AI behavior across consumer and enterprise contexts shapes data infrastructure. Measurement explains why features land differently for different audiences on Gamma's platform.

Requirements

The posting states a bachelor's degree requirement. Eight or more years of experience as a data scientist in product-focused tech companies is expected, including mentoring other data scientists. Leadership potential is considered for roles that evolve beyond individual contribution.

Statistical foundations are applied to design and analyze large-scale A/B tests and experiments with consistent rigor. Experiments validate product changes through methodical practices on Gamma's platform.

Experience building metrics frameworks from scratch using modern data stack tools like dbt and Snowflake is required. Comfort with unstructured or text data is necessary for analyzing AI outputs.

Background working with AI/LLM products and evaluating model performance in production is required. Evaluating generative AI shapes how success is measured in practice.

Complex technical concepts are communicated to non-technical stakeholders with clarity to influence product choices. Clear arguments are built from data and delivered to decision makers.

Practical notes

Flexibility for focused remote work is allowed when it supports deep work. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data science in product-focused environments often blends analysis with experimentation. Modern data stacks and AI evaluation shape how insights are generated. Clear communication turns technical findings into product actions.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.