Key Responsibilities
Benchmarking Methodology: Own the statistical methodology behind customer-facing benchmarking, end to end.
Comparability Modelling: Build and validate a model that determines which organizations are genuinely comparable, using features such as service mix, pricing, scale, and geography.
Defensibility Thresholds: Decide where the line sits between a comparison Onit can defend and one it cannot, and design the product's behaviour on both sides of it.
Data Quality Programme: Profile data quality across multiple source systems and lead a measurable improvement programme against a defined target, partnering with Data Engineering on remediation.
Entity Resolution: Define the approach for resolving duplicate and conflicting records across systems that were never designed to reconcile with one another.
External Data Assessment: Assess whether missing attributes can be responsibly supplemented from external sources, and set the evidential bar for doing so.
Governance & Privacy: Work with Product on methodology and governance for pooled, anonymised cross-customer analytics, including aggregation thresholds.
- Communicating Uncertainty: Explain uncertainty clearly to non-statisticians and help the product communicate confidence honestly to end users.
- Required Skills
- 7+ years in applied data science, analytics engineering, or a closely related quantitative role, including ownership of methodology for externally facing analytics.
Strong applied statistics: sampling, variance, confidence intervals, and sound judgement about small samples (e.g., able to explain why a three-item average may or may not be publishable).
Practical, production experience with entity resolution, record linkage, deduplication, or master data management.
Strong SQL, and Python or Ruby for analysis and modelling; hands-on experience working in Databricks (notebooks, Spark/PySpark, Delta tables) or a comparable cloud data platform.
Experience diagnosing and remediating data quality problems at scale, including reasoning about why data is missing before deciding what to do about it.
Rigorous validation habits: defines baselines and success metrics before building, and reports honestly against them.
Ability to communicate uncertainty to non-technical stakeholders without overstating confidence or being unhelpfully vague.
- Working proficiency with AI tools (e.g., Claude, GitHub Copilot) to accelerate analysis, code, and documentation, with the statistical judgment to critically validate AI-generated output.
- Preferred Qualifications
- Experience building benchmarks, rate comparisons, scoring models, or indices intended for external consumption.
Experience with anonymisation, aggregation thresholds, or privacy-preserving analytics.
Experience working with a cloud data warehouse or lakehouse (Databricks preferred).
Experience in a regulated or commercially sensitive domain, ideally legal.
Experience defining data quality SLAs or acceptance thresholds with business stakeholders.
