Principal Data Scientist

About this role

Data owns the pipelines, the warehouse and the definitions the business argues about — funnel conversion, time to hire, source quality.

Own the hardest measurement problems: what our matching actually does to outcomes, whether an intervention helped, and how to know when the observational data is lying.

The hard part is not the pipeline; it is that two teams asking the same question must get the same answer. Most of the work is in the semantic layer and in being willing to say a number is not trustworthy yet.

Primary responsibilities

• Set the technical direction for data science and own the hardest modelling problems
• Own the methodology behind matching and ranking, including how fairness is measured rather than asserted
• Set the standard for experimentation and causal inference across the company
• Grow the data science team through mentorship and review
• Be accountable for the statistical claims the company makes internally and externally

Skills required

Day to day this role works with Python, Machine Learning, Experimentation and Causal Inference.

• Twelve or more years in applied data science
• Strong on causal inference and experiment design, not only prediction
• Eight or more years in data science with a record of technical leadership
• Deep expertise in statistical methodology and machine learning
• Experience setting experimentation standards for an organisation
• A record of mentoring data scientists
• Accountability for statistical claims made externally