> Markdown version of [/jobs/ext/4708061-data-scientist](https://www.wearedevelopers.com/jobs/ext/4708061-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Scientist

- **Company:** Digital Waffle
- **Location:** New York, NY, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, BigQuery, Cursor, Python (Programming Language), NumPy, SQL Databases, Large Language Models, Snowflake, Jupyter, Pandas, Scikit Learn, Production Code, Claude, GPT, Databricks
- **Published:** November 13, 2026
- **Apply:** https://www.nettemps.com/us/en/W90D880646F358FB33F.ntap

## About the Role

* 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

## Description

* 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