Product Analyst - Data & Analytics

About the Company

Adalat AI is building an end-to-end justice tech stack that automates manual and clerical pain points in courtrooms, giving judges back time to focus on what matters most: decision-making and delivering justice. Our solutions — from AI-powered transcription in Indian languages to case-flow management, document navigation, and the Paperless Courts platform — are now deployed across 10 states, covering nearly 25% of India's judiciary. Backed by leading technology companies and funders, and incubated at MIT and Oxford, Adalat AI is working to eliminate judicial delays and expand access to timely justice. Founded by a team with backgrounds in law, technology, and economics from Harvard, Oxford, MIT, and IIIT Hyderabad, we are scaling rapidly across India and the Global South.

Role Overview

We are hiring a Product Analyst to own the truth of what happens inside our products.

Every dictation, template, and error in a courtroom is a data point. Today those data points are not trustworthy enough to decide with. Teams add instrumentation without telling anyone. The result is that the company is making decisions on numbers nobody has verified.

You will fix that, and then build on top of it. You own the data going in — the taxonomy, the review gate, the migration to our new analytics platform and warehouse. You also own the answers coming out — feature-level usage across states, the recurring report leadership plans around, and the analysis that settles arguments.

We have a preference (not a requirement) for people who have been the first analytics hire somewhere, with no standards to inherit. Our analytics function is young. We are not looking for someone to maintain a mature stack; we are looking for someone to build the thing that everyone else will later take for granted.

Key Responsibilities

Own data correctness — rebuild and document the event taxonomy, kill duplicates, and be the review gate through which every new event any team adds must pass
Catch drift before it ships — build alerting that detects new or changed events at the point of development, so we are never relying on teams to self-report
Run the migration — support the move to our new analytics platform and warehouse end to end: dual-send validation, export, dedupe, load
Build feature-level visibility — which feature, which state, which user type, trending which way, at a granularity that survives a CXO's follow-up question
Own the recurring reporting — the bi-weekly feature analytics report for leadership, and the state-level and feature-level reporting inside the partnership portal
Answer the questions that matter — correlation work against partnership OKRs across states, and one-off investigations where the answer changes a decision
Make the company self-serve — open up warehouse querying to other teams so routine questions stop routing through you

About You

You do not trust a number until you have checked how it was made. When someone shows you a chart, your first instinct is to ask what was filtered out. You have been burned before by a silent join, and you have not forgotten it.

You are fast with SQL and comfortable in Python, but you do not confuse tooling with the job. The job is that someone made a better decision because of you. A perfect query nobody acts on is a failure.

You write. Every number you produce comes with a sentence explaining what it means and, when it applies, a caveat you volunteer before anyone asks. You would rather say "I don't know yet, here is how I'd find out" to a CXO than guess confidently.

You are comfortable being the person who says no. Teams will want to ship events without review and pull numbers without context. Holding that line politely, every time, is a real part of this role.

Qualifications

1–3 years working with product or business data with real ownership
Strong SQL — you write window functions without looking them up, and you notice when a join changes your row count
Working Python or equivalent for cleaning, reconciliation, and one-off analysis
Hands-on with a product analytics tool — Mixpanel, Amplitude, PostHog, GA4 or similar — and a real understanding of event, property, and user schemas
Ability to build a dashboard that someone other than you actually uses
Clear, tight writing — memos and caveats people can act on
Genuine interest in AI and legal-tech, and in how Indian courts take up technology

Especially valuable

Warehouse experience (Azure, BigQuery, Snowflake, Redshift) and a transformation layer such as dbt
Having run a platform migration or a taxonomy cleanup before, and being able to describe what went wrong
Using LLM tooling for analysis; we are scoping an analytics agent
Experience in government, legal, public infrastructure, or other privacy-sensitive environments

What You Will Achieve in a Year

You will have made the event data trustworthy — documented, deduplicated, and gated, so no team adds instrumentation without review
You will have completed the migration to the new stack and warehouse, with the numbers reconciled and the old system retired
You will own the clearest read of product usage in the company, and leadership and partnership will plan around your reporting
You will have opened the warehouse to other teams, so the routine questions no longer come to you and you are free to work on the hard ones

Our Interview Process

We keep our process straightforward and transparent. Here's what to expect:

R1 — Intro Call (30 minutes)
An introduction to Adalat AI — our mission, the problem we're solving, and an initial conversation around role fit.

R2 — Design Deep-Dive (45 minutes)
A discussion on your past experience, design process, and how your background connects to the work we're doing.

R3 — Portfolio Review (60 minutes)
A walkthrough of your portfolio with a cross-functional panel — walk us through your work, decisions, and design thinking.

R4 — Culture Fit — Founder Chat (30 minutes)
A conversation with one or more of our founders to assess mutual fit and shared values.

R5 — Offer
If it's a great match on both sides, we'll move forward with an offer.

Note: The process may vary depending on seniority and role. For lead roles, a whiteboarding challenge may be added after the portfolio review.