Planned hiring
Data Scientist, Product
Lucy is a proactive AI coach from Lucy Labs that lives on your desktop as a cloud-backed app.
Planned hiring. This description explains the work we are preparing to hire for; it is not a confirmed start date or offer.
Make sure no one treats a dashboard movement as proof of learning or return on investment before the measurements support it. You will help the team determine whether people are gaining useful capability and whether that improvement produces better work outcomes. Your analyses will connect product decisions with evidence, including the uncertainty that comes with early customers and incomplete observations.
The work
Make sure no one treats a dashboard movement as proof of learning or return on investment before the measurements support it. You will help the team determine whether people are gaining useful capability and whether that improvement produces better work outcomes. Your analyses will connect product decisions with evidence, including the uncertainty that comes with early customers and incomplete observations.
Fewer prompt iterations might reflect improved skill, an easier task or premature acceptance of a poor answer. You will design measurements that distinguish these explanations before anyone treats a dashboard movement as proof of learning or return on investment.
What you will own
- Define decision-relevant measures of adoption, coaching usefulness, competency growth and work outcomes with Product and customer-facing colleagues. Document denominators, eligibility, observation windows and the evidence needed for each claim.
- Design experiments and observational analyses for selected coaching changes. Address selection effects, missingness, repeated observations and limited sample sizes; state when causal conclusions are justified and when they are not.
- Evaluate competency measurement against the framework's behavioral and assessment inputs. Preserve score lineage and confidence, test whether indicators reflect the intended capability, and flag conflicting definitions for resolution before relying on them.
- Analyze differences across tasks, experience levels and permitted customer contexts. Use interviews and field evidence to investigate explanations rather than treating every correlation as a product opportunity.
- Build reproducible analyses that another colleague can rerun. Review instrumentation and reusable metric definitions with Analytics Engineering, and trace important findings back to approved source data.
- Explain results to Product, engineering and customer teams in plain language. Provide supported conclusions, plausible alternatives and the next useful decision or study. Help keep customer value claims within the evidence.
Your partners
The Product Manager owns the product question and prioritization; you own measurement design and analytical interpretation. Analytics Engineering builds reusable transformations and metric models. Behavioral Science investigates intervention mechanisms and learning; Applied Science develops intent and recommendation methods. AI Evaluation independently measures model correctness, which is different from proving customer benefit. Work with Security and policy owners on permitted analysis, manager visibility and retention requirements.
What you bring
You have answered consequential product questions using statistics, SQL and reproducible analysis in Python or an equivalent language. Bring examples of experimental design, imperfect data and conclusions you changed when the evidence changed. Experience with longitudinal measurement, learning outcomes or enterprise products is helpful. You should be able to communicate uncertainty without making your work unusable and explain why a simple analysis is sometimes the right one.
Your first 90 days
Audit the measures proposed for one initial coaching use case and identify what can actually be observed. Agree a measurement specification with Product and Analytics Engineering. Deliver a reproducible baseline or pilot analysis with explicit limitations, and prepare an experiment or follow-up study that could resolve an important uncertainty. Document which claims the evidence supports and which remain hypotheses, so the next product and customer decisions have a clear foundation.
Next step
Express your interest
Email people@lucylabs.ai with the role title, a short introduction and relevant CV or portfolio. This is planned hiring; we will confirm current availability and the individual working arrangement.
Lucy Labs confirms the working arrangement, employer, location, eligibility, compensation and terms for each role. Our mailing address does not determine the job location or guarantee remote employment, worldwide eligibility or visa sponsorship.
Do not send passwords, government identifiers or bank details. Read the application privacy notice.
Frequently asked questions
Is this role open at Lucy Labs?
Lucy Labs lists this role as planned hiring. Availability depends on the need, team readiness and individual arrangement; it does not promise an immediate vacancy or start date.
Where does this work take place at Lucy Labs?
Lucy Labs confirms the working arrangement, employer, location, eligibility, compensation and terms for each role. Our mailing address does not determine the job location or guarantee remote employment, worldwide eligibility or visa sponsorship.
What does this role own at Lucy Labs?
Lucy Labs: Make sure no one treats a dashboard movement as proof of learning or return on investment before the measurements support it. You will help the team determine whether people are gaining useful capability and whether that improvement produces better work outcomes. Your analyses will connect product decisions with evidence, including the uncertainty that comes with early customers and incomplete observations.
How do I express interest in Lucy Labs?
Email people@lucylabs.ai with the role title, a short introduction and relevant CV or portfolio. Lucy Labs uses the application to assess and respond to it; retention for future opportunities is a separate explained purpose and choice.
