Data Scientist, Product
Job description
About the role
You will own the outcomes for a product area at Lovable, a platform that lets anyone build software in any language, used by millions of people from solopreneurs to Fortune 100 teams to transform raw ideas into real products fast. Your work centers on finding where users struggle or leave, forming product bets from those findings, and seeing those bets through to shipment. You design the measurement and experimentation infrastructure that product teams depend on, and you build systems that surface opportunities without requiring you to be asked each time. The role demands someone who thinks like a product owner, taking full responsibility for metrics from hypothesis to shipped impact, in a culture of extreme ownership, high velocity, and low-ego collaboration, as part of a small, talent-dense team building a generation-defining company from Stockholm.
Key facts
What you'll do
Own the metrics and funnels tied to a product area, and push measurable gains in activation, engagement, and retention.
Identify patterns in user behavior and convert them into product bets, partnering directly with PMs and engineers to ship changes.
Design and run experiments, then act on the results quickly to guide product decisions and prioritize roadmap.
Construct the instrumentation, semantic models, and agents that the product team relies on daily.
Ensure the data the team trusts remains trustworthy by maintaining quality, clear definitions, and full lineage.
Work closely with product and engineering teams in a high-velocity, low-ego environment.
Build proactive monitoring systems that flag opportunities before anyone asks for an analysis.
Translate complex findings into directional answers that ship quickly while knowing when deeper rigor is needed.
Collaborate with the analytics and product teams to define and refine key performance indicators.
Own the end-to-end lifecycle of insights, from hypothesis to experiment to shipped product change.
Partner with the analytics team to define tracking plans and ensure consistent event taxonomy across the product.
Mentor junior analysts and data team members by sharing best practices and fostering a culture of data-driven decisions.
Requirements
Strong SQL skills with the ability to write complex queries for analysis and modeling at scale.
Strong proficiency in Python for data analysis, scripting, and building reliable data pipelines.
Applied statistics knowledge, including experimentation design, A/B testing, and growth testing methods for product decisions.
Deep instinct for user behavior patterns around activation, engagement, and long-term retention.
Comfortable shipping directional answers fast and recognizing when a decision needs more rigorous statistical validation.
An entrepreneurial mindset that thrives with autonomy and ambiguity in fast-moving environments.
Experience working shoulder to shoulder with product and engineering teams to ship changes.
A product-minded approach where you own outcomes end to end rather than just handing off insights to others.
Nice to have
Experience with GCP services such as BigQuery and PubSub for data warehousing, event streaming, and real-time analytics.
Familiarity with analytics tools like Hex or product platforms such as Lovable Apps.
Background in building agents or automated systems that surface insights proactively for teams.
Exposure to enterprise-scale products with hundreds of millions of monthly visits, a compounding enterprise footprint, and cross-functional impact.
Skills & tools
SQL and Python as the primary languages for data analysis, modeling, and building production-ready pipelines.
BigQuery and PubSub for data warehouse and event processing at scale.
Hex and Lovable Apps for analytics, product analytics, and building interactive dashboards and reports.
A/B and growth testing for experimentation and validation of product changes.
GCP as the cloud infrastructure layer supporting all data workloads, storage, and compute needs.
Instrumentation and semantic modeling for building trusted and reliable data systems.
Practical notes
The hiring process begins with filling out a short form and jumping on an intro call with our recruiting team.
There is a call with the hiring team member followed by a take-home case study.
A Most Impressive Project session is part of the evaluation, where you share a project you are proud of.
Cross-functional interviews with the people you would work with are included in the process.
A final conversation with leadership wraps up the hiring process.
Applications must be submitted in English, as it is the company language and the language you will use daily.
Lovable treats all candidates equally and welcomes applications through their careers portal.