> Markdown version of [/jobs/ext/2074827-data-scientist-product](https://www.wearedevelopers.com/jobs/ext/2074827-data-scientist-product). 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, Product

- **Company:** Lovable Labs Incorporated
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, BigQuery, Python (Programming Language), Standard Sql, SQL Databases, Google Cloud
- **Published:** August 15, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pb8wirsyq1

## About the Role

Please submit your application in English. It's our company language, so you'll be speaking lots of it if you join.

## Description

* A product-minded data scientist who owns a product area's outcomes: you find the opportunity, form the bet, and see it shipped, rather than handing insights to a PM.
 * You build systems, not one-offs: the instrumentation, metrics, and agents that produce insight and flag opportunities proactively.
 * Strong SQL, Python, applied statistics, and experimentation (A/B and growth tests).
 * Deep instinct for user behavior: activation, engagement, retention, and what moves them.
 * Comfortable shipping directional answers fast, and knowing when a decision needs rigor.
 * Entrepreneurial. Thrives with autonomy and ambiguity, and works shoulder to shoulder with product and engineering.

What You'll Do

 * Own the metrics and funnels for a product area, and drive measurable improvement in activation, engagement, and retention.
 * Find opportunities in user behavior and turn them into product bets, working directly with PMs and engineers to ship them.
 * Design and run experiments, and act on the results.
 * Build the instrumentation, semantic models, and agents the product team runs on.
 * Keep the data the team trusts trustworthy: quality, definitions, and lineage.

Our tech stack

We're building with tools that both humans and AI love:

 * Languages: SQL and Python
 * Warehouse & events: BigQuery, PubSub
 * Analytics & product: Hex, Lovable Apps
 * Experimentation: A/B and growth testing
 * Cloud: GCP

And always on the lookout for what's next.

How We Hire

 * Fill in a short form and jump on an intro call with our recruiting team
 * A call with the hiring team member
 * A take-home case study
 * A Most Impressive Project session
 * Cross-functional interviews with the people you'd work with
 * A final conversation with leadership


## Related Videos

- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [The Cloud is Calling: Answer with In-Demand Skills](https://www.wearedevelopers.com/videos/945-the-cloud-is-calling-answer-with-in-demand-skills)
- [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one)
- [Under the Hood of Building on Lovable](https://www.wearedevelopers.com/videos/100032-under-the-hood-of-building-on-lovable)
- [Making Data Warehouses fast. A developer's story.](https://www.wearedevelopers.com/videos/302-making-data-warehouses-fast-a-developer-s-story)
- [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai)

## Related Articles

- [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)
- [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)