Data Analytics Team Lead.

About Incode

Incode is a Series B unicorn rewriting how the world proves identity. Our AI-powered platform lets leading banks, fintechs, marketplaces, and governments deliver friction-free experiences while defeating fraud and safeguarding privacy. Customers such as Citi, AirBnB, Block, Chime, Sixt, and TikTok rely on Incode to power their identity verification and security.

Recently named a Leader in the Gartner® Magic Quadrant™ for Identity Verification, we're scaling fast and we're looking for an Analytics Team Lead to make the quality of our identity technology visible, measurable, and relentlessly data-driven.

The Impact You'll Make

This role sits on the Document Intelligence team — the group behind Incode's industry-leading document processing. Our technology parses identity documents from around the world — passports, visas, driver's licenses, proof of address, and more — for some of the biggest enterprises on the market. Machine learning is the core of the product, and Analytics is what makes its quality visible and its roadmap data-driven.

We're looking for a Senior Lead of Data Analytics who is, first and foremost, a sharp hands-on analyst — and who has the leadership experience to grow a small, high-impact team around them. Today the team is three people; you'll be the fourth, owning it end-to-end. This is a true player-coach role: you'll manage and mentor the team while personally writing the pipelines, queries, and analyses. Our analytics is partially built — some data pipelines and testing procedures are already in place, but metric coverage and visibility are far from complete. Your mission is to close that gap.

What You'll Own & Drive

• Lead & Grow the Team — Run the analytics team as a formal manager: own the roadmap, hold 1:1s and performance reviews, develop your analysts, and participate in hiring as the team scales.
• Stay Hands-On — This role involves a lot of personal execution. You'll write SQL and Python pipelines for data collection, processing, and labeling — and do the analysis yourself.
• Expand the Metric Tree — Build out the end-to-end metric tree that measures product quality across the document-processing pipeline, closing coverage gaps where the product is currently blind.
• Build Visibility — Create reporting and dashboards that surface weak points and turn raw signals into clear insights for ML, product, and leadership.
• Own ML Quality Evaluation — Drive data preparation for ML, model quality assessment, and benchmarking, in tight collaboration with the ML team.
• Drive Experimentation — Design, run, and interpret A/B tests across mobile and server side, with genuine statistical rigor.
• Guard Data & Labeling Quality — Monitor accuracy and consistency, and build the controls that keep them high.
• Bridge to Engineering — When events or data are missing, pinpoint exactly what's needed and where it should come from, write clear and actionable tasks for the backend and data teams, and justify their prioritization in business terms.
• Systematize — Turn one-off analyses into repeatable, owned pipelines and reporting cadences.

Your Background

• Team leadership experience, paired with a genuine appetite to stay hands-on — you lead and you build.
• Strong Python, with a bias toward clean, maintainable, well-structured code.
• Strong SQL and solid experience with columnar / analytical databases.
• Strong statistics and experimental design — you can design tests (A/B and beyond), reason about significance and power, and interpret results correctly.
• Modern data tooling — experience with workflow orchestration, data transformation, cloud storage, and experiment-tracking. Our stack is Python, S3, Redshift, ClearML, Airflow, and dbt; equivalent tools are perfectly fine — what matters is that you've worked with this kind of stack.
• Excellent communication — you can translate a data gap into a prioritized, business-justified requirement, talk to engineers and stakeholders in their own language, and make the case for what gets built.
• High ownership, autonomy, and pragmatism — comfortable prioritizing impact over perfection in an evolving environment.

Bonus points for:

• Experience with ML-centered products — evaluating model quality and working alongside ML teams. You won't be expected to train production models; at most simple models for data filtering, plus the ability to run existing ones.
• Familiarity with data labeling processes and quality estimation/maintenance.
• Domain exposure to identity documents, OCR, or computer-vision pipelines.

The Qualities That Set You Apart

• A player-coach at heart — you draw energy from both growing people and doing the work yourself.
• Craftsmanship in how you build — clean code, sound pipelines, and analyses others can trust and reuse.
• Intellectual honesty and statistical rigor — you follow the data where it leads and know when a result is real.
• A translator's instinct — you turn ambiguous signals into clear insight and make the business case for what to build next.
• Pragmatism and high ownership — you thrive in an evolving environment and push for impact over perfection.

Why Incode?

Mission with Meaning — Shape how billions of people prove identity — safely, simply, and ethically.

Rocket-Ship Growth — Join at a defining moment where your work compounds in global impact.

Elite Team & Technology — Collaborate with top-tier engineers, researchers, and scientists at the forefront of AI.

Ownership & Autonomy — Operate with the freedom to test, build, and ship your ideas end-to-end.

Global Impact — Your work will shape secure, frictionless identity verification experiences across continents.