Applied Machine Learning Manager - Application Fraud

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Northern (KY)

Hybrid

USD 200,000 - 250,000

Full time

2 days ago

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Benefits offered by this job

Health insurance for you and dependets

401(k) plan with employer match

Flexible paid time off

In-person company events

Home office stipend

Job summary

SentiLink builds the fraud detection and identity verification models that power a large part of the US financial system. As an Applied ML Manager, you own a core area end to end and lead a growing team of ML scientists focused on high-impact outcomes.

You’ll coach, review code, and contribute hands-on while partnering with Product, Engineering, and Risk to deliver scalable ML solutions. The role emphasizes AI safety and governance.

Qualifications

6+ years of industry experience applying ML or statistics to real-world problems, including 3+ years directly managing ML/data science teams.
Experience leading ML teams in fraud/identity/fintech or risk-focused domains, with interest in product ownership for that domain.
Demonstrated success deploying production ML models and writing production-quality Python code and tests.
Strong practical ML and statistics knowledge with ability to scope solutions quickly.
Comfortable end-to-end work and delivering measurable business impact.
Familiar with modern LLMs and AI-assisted development workflows; capable of handling sensitive data with governance constraints.
Excellent communication and mentoring skills; capable of high-stakes decisions and cross-functional collaboration.
Candidates must be legally authorized to work in the United States and reside in the United States.

Responsibilities

Directly manage a team of applied ML scientists (4 today, 6 by end of 2026) and set engineering/modeling practices.
Own execution for your area: priorities, delivery, and results.
Coach and mentor team members through model development, experimentation, and technical decisions; remain hands-on where it matters.
Partner with senior leadership, Product, Engineering, and Risk to prioritize work and deliver impactful ML solutions.
Communicate progress and tradeoffs clearly; represent domain in product strategy discussions.
Develop and improve fraud detection and identity models across data lifecycle: acquisition, feature engineering, labeling, training, deployment, monitoring.
Research new fraud patterns and design analyses to inform product and business decisions.
Use AI across the team’s work and push where AI belongs in products.

Skills

Python

Machine Learning

Statistics

Data Science

Leadership

Mentoring

Education

Bachelors/Masters/PhD in CS/Statistics/Math/Physics

Tools

PostgreSQL

AWS

XGBoost

scikit-learn

pandas

Elasticsearch

OpenSearch

Neo4j

MLflow

Flyte

LLM tooling

Job description

SentiLink provides innovative identity and risk solutions, empowering institutions and individuals to transact with confidence. We’re building the future of identity verification in the United States replacing a clunky, ineffective, and expensive status quo with solutions that are 10x faster, smarter, and more accurate.

We’ve seen tremendous traction and are growing extremely quickly. Our real-time APIs have helped verify hundreds of millions of identities, starting with financial services and rapidly expanding into new markets. SentiLink is backed by world‑class investors including Craft Ventures, Andreessen Horowitz, NYCA, and Max Levchin.

We’ve earned recognition from TechCrunch, CNBC, Bloomberg, Forbes, Business Insider, PYMNTS, American Banker, LendIt, and have been named to the Forbes Fintech 50. We have also been named a 2026 FICO Industry Vanguard Decision Award Winner. Last but not least, we’ve even made history - we were the first company to go live with the eCBSV and testified before the United States House of Representatives on the future of identity.

SentiLink supports a variety of ways to work, ranging from fully remote to in-office. We operate as a digital‑first company with strong collaboration across the U.S. and India. We maintain physical 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. If you’re located near one of these offices, we would love for you to spend time in the office regularly. Some roles are hybrid or in-office by design. For example, our engineering team in India works primarily from our Gurugram office.

Role:

SentiLink builds the fraud detection and identity verification models much of the US financial system runs on. As an Applied ML Manager, you own a core area end to end.

You’ll lead a team of 4 applied ML scientists, 6 by the end of 2026, all experienced and technically deep enough to challenge you daily. This is a people management role that stays close to the work: you’ll coach, review code, and contribute directly where it matters most.

Data science drives product decisions here, and the growth path from this role is strategic leadership in both the product and ML domains you own. We use AI across all of our work, are exploring where it belongs in the products themselves, and hold a hard line on AI safety and data governance.

Technologies: Python 3, PostgreSQL, AWS, XGBoost, scikit-learn, pandas, Elasticsearch and OpenSearch, Neo4j, MLflow, Flyte, and use of modern LLM tooling.

Responsibilities:

Directly manage a team of applied ML scientists, 4 today and growing to 6 by the end of 2026, and help set the engineering and modeling practices they work by.
Own execution for your area: priorities, delivery, and the results your team produces.
Coach and mentor team members through model development, experimentation, and technical decision making, and remain hands‑on where it matters most, including code review and production systems.
Partner with senior leadership, Product, Engineering, and Risk to prioritize work and deliver impactful ML solutions.
Communicate progress, tradeoffs, and results clearly, and grow into representing your domain in product strategy discussions.
Develop and improve SentiLink's fraud detection and identity models across the full lifecycle: data acquisition, feature engineering, labeling strategy, model training, experimentation, production deployment, monitoring, and iteration.
Research emerging fraud patterns, build new ML capabilities for identity verification and financial risk, and design analyses that inform product and business decisions.
Use AI throughout your team's work, help push the boundary on what that unlocks, and contribute to our exploration of where AI belongs inside the products themselves.

Requirements:

6+ years of industry experience applying machine learning or statistics to real‑world problems, including 3+ years directly managing machine learning or data science teams. Experience at high-growth startups preferred.
Experience leading ML or data science teams in fraud, identity, fintech, banking, financial services, payments, or adjacent risk-focused domains, and interest in growing into ownership of product direction for that domain.
Bachelor's, Master's, or PhD in Computer Science, Statistics, Mathematics, Physics, or another quantitative discipline.
Demonstrated success developing and deploying production machine learning models, plus experience writing production-quality Python code and tests.
Strong practical ML and applied statistics knowledge: able to scope solutions quickly with standard tooling and go deep where it pays off.
Comfortable working end to end, with a track record of measurable business impact: planning, defining success criteria, getting buy-in, building the solution, and delivering it, whether in production, in a deck, or as strategy.
Comfortable with modern LLMs and AI‑assisted development workflows and interested in pushing them further. Sound judgment when working with sensitive data under real information security and data governance constraints.
Strong communicator who enjoys mentoring others while remaining technically hands‑on. Detail oriented and thoughtful, someone we can rely on to make high‑stakes calls while thriving on varied, open‑ended, high‑impact problems.
Candidates must be legally authorized to work in the United States and must live in the United States.