Data Scientist (Legal Tech AI)

Location: Pune, Maharashtra — Hybrid workplace

hirify.global builds an AI-centric legal operations platform to help legal departments become faster, smarter, and more strategic.

What you will do

• Own the statistical methodology behind customer-facing benchmarking end to end.
• Build and validate a comparability model using features such as service mix, pricing, scale, and geography.
• Defend the line between publishable and non-publishable comparisons and design product behaviour on both sides.
• Profile data quality across multiple source systems and lead a measurable improvement programme with Data Engineering.
• Define the approach for resolving duplicate and conflicting records across systems never designed to reconcile.
• Work with Product on governance for pooled, anonymised cross-customer analytics, including aggregation thresholds, and communicate uncertainty clearly to non-statisticians.

Requirements

• 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.
• Practical production experience with entity resolution, record linkage, deduplication, or master data management.
• Strong SQL plus Python or Ruby; hands-on Databricks experience (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.
• 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.
• Working proficiency with AI tools (e.g., Claude, GitHub Copilot) with the statistical judgement to critically validate AI-generated output.

Nice to have

• Experience building benchmarks, rate comparisons, scoring models, or indices for external consumption.
• Experience with anonymisation, aggregation thresholds, or privacy-preserving analytics.
• Experience 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.

Culture & Benefits

• Exceptional paid leave for birth parents, non-birth parents, and caregivers, with surrogacy and adoption reimbursement.
• 100% employer-paid life and disability insurance.
• Values-driven culture: customer first, purposeful innovation, teamwork, and intentional growth.