Data Scientist

Digital Waffle; San Francisco (CA); On-site; USD 120,000 - 190,000; Full time

Job summary

Digital Waffle is looking for a Data Scientist in San Francisco to turn messy early-stage data into decisions that move the business. You’ll work directly with founders, product and engineering to design experiments and surface insights from product telemetry, running end-to-end analyses in a fast-paced startup setting.

This role sits in-office five days a week, collaborating across product, growth and retention surfaces to influence real roadmap and GTM decisions.

Qualifications

• 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 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, SQL and notebook environments (Jupyter, Hex).
• Build and maintain analytical layers in dbt against a warehouse.
• Partner with product and engineering on event instrumentation and tracking plans.
• Use AI tooling 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

Pandas

NumPy

scikit-learn

Jupyter

Tools

dbt

Snowflake

BigQuery

Databricks

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.