Data Scientist, Growth

About the role

You will be the first data scientist on the newly formed London growth team at Lovable. The company enables anyone to build software in any language, serving millions of users ranging from individual solopreneurs to large Fortune 100 organizations across the globe. Lovable-built applications and websites receive hundreds of millions of visits each month, and the enterprise footprint is compounding at a rapid pace. You will own the full growth funnel end to end and drive measurable outcomes rather than providing decision support or one-off analyses.

Key facts

What you'll do

Own the complete growth funnel metrics end to end, spanning acquisition, activation, retention, and expansion stages.

Identify the highest-leverage opportunities in the funnel and turn them into experiments that ship to users.

Work directly with growth engineers to design, build, and deploy growth experiments into the live product.

Design, execute, and interpret A/B and growth tests rigorously, then act decisively on the results.

Build the instrumentation, metrics frameworks, and automated agents that the growth team runs on daily.

Proactively surface growth opportunities using data systems and agents rather than relying on one-off ad hoc analyses.

Help define and shape the growth data function as its founding data scientist from the very start.

Ship experiments quickly and often, balancing directional reads with the statistical rigor that matters most.

Collaborate with cross-functional teams across product, engineering, and design to align data work with business priorities.

Drive measurable improvements across every stage of the user lifecycle and funnel through continuous experimentation and iteration.

Requirements

Strong command of SQL and Python for data analysis, statistical modeling, and querying large production datasets.

Deep understanding of applied statistics, hypothesis testing, confidence intervals, and experimental design for growth testing.

Proven experience running A/B and growth experiments in a fast-moving, product-oriented environment with shipping pressure.

Demonstrated ability to build proactive data systems and automated agents that surface growth opportunities continuously.

Comfortable making decisions with directional data reads and knowing precisely when deeper statistical rigor is needed.

An entrepreneurial mindset with a bias toward autonomy, ownership, and shipping meaningful work quickly and often.

A strong instinct for what drives acquisition, activation, retention, and expansion metrics in practice and at scale.

Experience working closely with engineering teams to ship data-driven product changes, growth experiments, and measurable improvements.

Nice to have

Familiarity with BigQuery, PubSub, or other GCP data infrastructure and event streaming pipelines at scale.

Experience using Hex or similar analytics and product analytics platforms to explore and surface insights.

Background in building data agents or automated insight systems that proactively surface growth opportunities.

Exposure to AI-assisted software development tools and workflows, especially in a production or consumer-facing setting.

Understanding of growth metrics frameworks including acquisition funnels, activation cohorts, retention curves, expansion loops, and cohort analysis.

Skills & tools

SQL and Python for data analysis, statistical modeling, querying, and building data pipelines.

BigQuery as the primary data warehouse for event storage, analytics, and large-scale data processing.

PubSub for event streaming, real-time data ingestion, and building reliable data pipelines.

Hex as a platform for analytics, interactive dashboards, and product-level insights exploration.

Lovable Apps for building internal tools, dashboards, data views, and custom analytics applications.

A/B and growth testing frameworks and methodologies for designing and running rigorous experiments.

Google Cloud Platform for cloud infrastructure, deployment, orchestration, and production-grade services.

Experimentation culture and practices including hypothesis-driven testing, metric definition, causal inference, and statistical rigor.

Practical notes

All applications must be submitted in English, as it is the company language and the working language.

Lovable treats all candidates equally and welcomes diverse applicants from all backgrounds and all experiences.

Please apply through the company careers portal to be considered for this specific position.

The hiring process includes an intro call, hiring manager call, take-home case study, Most Impressive Project session, cross-functional interviews, and a final leadership conversation.

The company is based in Stockholm and is growing a new team in London for this role.

The company values extreme ownership, high velocity, and low-ego collaboration among all team members every day.