Job description

SentiLink sentilink.com helps institutions and protects customers by accurately resolving identities and flagging fraud risk. Each day, we verify more than 3 million identities, flagging 150k+ fraud attempts in the process. The problems we work on are challenging, and solving them takes deep domain expertise, rigorous analysis, and a willingness to dig into the details. At SentiLink, you'll work alongside smart, highly collaborative colleagues who respect each other's time and judgment, take ownership, and follow through on their commitments. Together, we do truly meaningful work protecting the identities of innocent consumers and flagging the fraudsters attempting to exploit them. We're well capitalized and growing quickly. We already serve 13 of the top 15 U.S. banks, we're expanding into new markets, and we're backed by Craft Ventures, Andreessen Horowitz, NYCA, and Max Levchin. We've been named to the Forbes Fintech 50 forbes.com and as a 2026 FICO Industry Vanguard Decision Award Winner businesswire.com. We were also the first company to go live resources.sentilink.com with the electronic Consent Based Social Security Number Verification (eCBSV) service, and we've testified before the U.S. Congress resources.sentilink.com on the future of identity. We want SentiLink to be the best place you've ever worked. We offer generous benefits and support a range of working arrangements, from fully remote to in-office. We are a digital-first company with strong collaboration across the U.S. and India, and we meet in person regularly to build relationships. We have offices in Austin, San Francisco, New York City, Seattle (Bellevue), Los Angeles, and Chicago in the U.S., and in Gurugram (Delhi) and Bengaluru in India. ROLE: SentiLink builds the fraud detection and identity verification models much of the US financial system runs on. Each one has to pass review by our customers' model risk teams, regulators, and auditors without slowing how fast we ship. You'll own that domain end to end. You'll lead a team of 3+ data scientists. You won't build our fraud products; your team measures, stress-tests, explains, and monitors them. It's a management role that stays hands-on: you'll review code, challenge methodology, and step in where needed. The role includes real modeling work: assurance models that audit our production models, find gaps in how our products behave, and produce unbiased evaluation metrics. We use AI in model risk today, but mostly in one-off projects. You'll make it a first-tier tool that scales what the team can do, putting you at the cutting edge of AI in model risk and governance. You'll also work directly with model risk teams at some of the biggest banks and financial institutions in the space. Governance gates how fast they adopt our products, so this role is a direct lever on growth. This is a remote, US-based role. Technologies: Python, PostgreSQL, AWS (S3, Redshift, SageMaker), pandas, scikit-learn, XGBoost, MLflow, and modern LLM tooling. WHAT YOU'LL DO: - Manage a team of 3+ data scientists and set how they work. - Own strategy and execution for model risk: roadmap, priorities, and what we automate or standardize. - Own the technical core: performance and drift monitoring, validation, calibration and stability testing, fair lending and bias assessments, model inventory, and change management. - Build assurance models that audit production models, surface product gaps, and produce unbiased evaluation metrics. - Move AI from one-off projects to a first-tier tool across the team's work. - Act as an independent technical counterpart to Applied ML: review methodology and translate model changes into risk impact. - Produce validation reports and documentation for leadership, customers, auditors, and regulators, and track findings to remediation. - Work with partner model risk teams at major banks and FIs, alongside Partner Success. This is a high-leverage role. Governance gates how quickly our customers can adopt what we build, which makes it a direct lever on the company's growth, with substantial room for the right person to define it and grow with it. WHAT WE'RE LOOKING FOR: - 6+ years applying statistics, ML, or quantitative analytics in industry, including 3+ years managing DS, analytics, or quant risk teams. - Background in at least one of: model or portfolio risk analytics; modeling in a supporting or consulting role, internal or external; large-scale analytics, reporting, or BI. - Degree in Statistics, Math, CS, Economics, Physics, or another quantitative field. - Strong hands-on Python, including building recurring pipelines, monitoring, or reporting. Working knowledge of Git and SQL. - Experience with cloud data and compute: object storage like S3 and remote compute like SageMaker or Databricks. AWS preferred. - Able to read a model and interrogate its methodology: performance evaluation, calibration, drift, backtesting, and fairness. - Track record of measurable business impact, end to end. - Proficient with AI tools in daily work: effective prompting, building and using agent skills, and AI-assisted coding and analysis. - Sound judgment with sensitive data, and clear communication with technical and non-technical audiences. - Bachelor's degree in a quantitative field (Math, Statistics, CS, Engineering, Economics, or related STEM) - Must be legally authorized to work in and reside in the US NICE TO HAVES: - Familiarity with SR 11-7, SR 26-2, OCC guidance, or fair lending. - Experience working with model risk teams at banks or fintechs, auditors, or regulators. - Fraud, identity, credit, or financial risk model experience. - Experience building or scaling a validation, governance, or analytics function. COMPENSATION: $210,000-$240,000/year + equity + benefits PERKS: - Employer paid group health insurance for you and your dependents - 401(k) plan with employer match (or equivalent for non US-based roles) - Flexible paid time off - Regular company-wide in-person events - Home office stipend, and more! CORPORATE VALUES: - Follow Through - Deep Understanding - Whatever It Takes - Do Something Smart

Requirements

Must-haves

The role includes real modeling work: assurance models that audit our production models, find gaps in how our products behave, and produce unbiased evaluation metrics.
We use AI in model risk today, but mostly in one-off projects. You'll make it a first-tier tool that scales what the team can do, putting you at the cutting edge of AI in model risk and governance.
You'll also work directly with model risk teams at some of the biggest banks and financial institutions in the space. Governance gates how fast they adopt our products, so this role is a direct lever on growth.
This is a remote, US-based role.
Technologies: Python, PostgreSQL, AWS (S3, Redshift, SageMaker), pandas, scikit-learn, XGBoost, MLflow, and modern LLM tooling.
Manage a team of 3+ data scientists and set how they work.
Own strategy and execution for model risk: roadmap, priorities, and what we automate or standardize.
Own the technical core: performance and drift monitoring, validation, calibration and stability testing, fair lending and bias assessments, model inventory, and change management.
Build assurance models that audit production models, surface product gaps, and produce unbiased evaluation metrics.
Move AI from one-off projects to a first-tier tool across the team's work.
Act as an independent technical counterpart to Applied ML: review methodology and translate model changes into risk impact.
Produce validation reports and documentation for leadership, customers, auditors, and regulators, and track findings to remediation.
Work with partner model risk teams at major banks and FIs, alongside Partner Success.
6+ years applying statistics, ML, or quantitative analytics in industry, including 3+ years managing DS, analytics, or quant risk teams.
Background in at least one of: model or portfolio risk analytics; modeling in a supporting or consulting role, internal or external; large-scale analytics, reporting, or BI.
Degree in Statistics, Math, CS, Economics, Physics, or another quantitative field.
Strong hands-on Python, including building recurring pipelines, monitoring, or reporting. Working knowledge of Git and SQL.
Able to read a model and interrogate its methodology: performance evaluation, calibration, drift, backtesting, and fairness.
Track record of measurable business impact, end to end.

Nice-to-haves

Experience with cloud data and compute: object storage like S3 and remote compute like SageMaker or Databricks. AWS preferred.
Familiarity with SR 11-7, SR 26-2, OCC guidance, or fair lending.
Experience working with model risk teams at banks or fintechs, auditors, or regulators.
Fraud, identity, credit, or financial risk model experience.
Experience building or scaling a validation, governance, or analytics function.

Application prep

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Benefits

Benefits detected in the listing.

Health Insurance

401(K)

Paid Time Off