Data Scientist

About the role

About Onit

We're redefining the future of legal operations through the power of AI. Our cutting-edge platform streamlines enterprise legal management, matter management, spend management and contract lifecycle processes, transforming manual workflows into intelligent, automated solutions.

We’re a team of innovators using AI at the core to help legal departments become faster, smarter, and more strategic. As we continue to grow and expand the capabilities of our new AI-centric platform, we’re looking for bold thinkers and builders who are excited to shape the next chapter of legal tech.

If you're energized by meaningful work, love solving complex problems, and want to help modernize how legal teams operate, we’d love to meet you.

Position Summary

The Data Scientist owns the statistical methodology behind a new customer-facing benchmarking capability in Onit's AI-driven legal operations platform. Drawing on data from multiple source systems across enterprise legal management, matter management, and spend management, this role determines which organizations are genuinely comparable, decides where the line sits between a defensible comparison and one that is not, and ensures the product communicates confidence honestly to end users. Working closely with Product, Engineering, and the Data Architect on a Databricks-based platform, the Data Scientist sets the evidential standards, data quality targets, and governance for pooled, anonymised cross-customer analytics.

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.