Data Scientist

Digital Waffle; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Digital Waffle in San Francisco is seeking a Data Scientist to turn messy early-stage data into decisions that move the business. You’ll work directly with founders, product and engineering to design experiments, surface insights from product telemetry, and run end-to-end—from data pulls to presenting results that drive decisions within the week.

You’ll design experiments across product, growth and retention, build models in Python and SQL, and collaborate on event tracking with a lean,

Qualifications

• Strong Python and SQL for day-to-day analytical work.
• Experience with experimentation, causal inference or statistical modelling in a product environment.
• Comfortable with dbt and a modern data warehouse (Snowflake, BigQuery, Databricks).
• Fluent with Pandas, NumPy, scikit-learn and notebook tools like Jupyter or Hex.
• Fluent in modern AI tooling (Claude, Cursor, ChatGPT) and able to use it to raise output.
• High autonomy, strong communication, pragmatic approach to messy early-stage data.
• Happy working 5 days a week in-person with a small, high-intensity team.

Responsibilities

• Design and analyse experiments across product, growth and retention surfaces
• Build models and analyses that influence real roadmap and GTM decisions
• Work hands-on in Python (Pandas, NumPy, scikit-learn), SQL and notebook environments (Jupyter, Hex)
• Build and maintain analytical layers in dbt against a warehouse (Snowflake, BigQuery or DuckDB-style stack)
• Partner with product and engineering on event instrumentation and tracking plans
• Use AI tooling (Claude, Cursor and similar) across every part of your analytical workflow to compress cycle time
• Present findings clearly to founders and stakeholders, with sharp written and verbal communication

Skills

Python

SQL

Experimentation

Causal inference

Statistical modelling

AI tooling

Communication

Tools

dbt

Snowflake

BigQuery

Databricks

Jupyter

Hex

Job description

A high-growth AI-native startup is hiring a Data Scientist in San Francisco to turn messy early-stage data into decisions that actually move the business.

This isn’t a reporting role. You’ll work directly with founders, product and engineering to design experiments, surface insight from product telemetry and model user behaviour. Expect to run end to end: pulling data, building the analysis, presenting it, and seeing the decision made in the same week.

Role: Data Scientist

Location: San Francisco, 5 days per week in office

What you’ll be doing:

• Design and analyse experiments across product, growth and retention surfaces
• Build models and analyses that influence real roadmap and GTM decisions
• Work hands-on in Python (Pandas, NumPy, scikit-learn), SQL and notebook environments (Jupyter, Hex)
• Build and maintain analytical layers in dbt against a warehouse (Snowflake, BigQuery or DuckDB-style stack)
• Partner with product and engineering on event instrumentation and tracking plans
• Use AI tooling (Claude, Cursor and similar) across every part of your analytical workflow to compress cycle time
• Present findings clearly to founders and stakeholders, with sharp written and verbal communication

What you’ll need:

• Strong Python and SQL for day-to-day analytical work
• Solid experience with experimentation, causal inference or statistical modelling in a product environment
• Comfortable with dbt and a modern warehouse (Snowflake, BigQuery, Databricks or similar)
• Fluent with Pandas, NumPy, scikit-learn and notebook tools like Jupyter or Hex
• Fluent in modern AI tooling (Claude, Cursor, ChatGPT) and able to use it to materially raise your output
• High autonomy, strong communication, pragmatic approach to messy early-stage data
• Happy working 5 days a week in-person with a small, high-intensity team

Nice to have:

• Prior early-stage startup experience with no formal data team to lean on
• Exposure to LLM evals, user behaviour modelling on AI products or agent telemetry
• Comfort shipping lightweight production code when the analysis needs to live somewhere

This role would suit a data scientist who wants to sit close to the decision, not three handoffs away from it.