Head of Applied Machine Learning – Application Fraud

🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $210k - $260k / year

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 20%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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SentiLink

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2017

🔐 Security

💳 Fintech

💸 Finance

💰 $70M Series B on 2021-08

Security • Fintech • Finance

SentiLink is a company that partners with leading financial institutions to tackle identity theft and fraud at the point of application. Their solutions incorporate expert analysis and machine learning models to identify and manage fraud effectively. SentiLink targets various types of fraud including synthetic, identity theft, and first-party fraud, and provides flexible integration options such as APIs to suit diverse customer needs. With a focus on data security and a customer-centric approach, SentiLink delivers real-time insights and solutions to financial institutions and fintech companies, enhancing their ability to approve legitimate customers while minimizing fraud.

📋 Description

• Directly manage a team of applied ML scientists, growing from 4 to 6 by the end of 2026 • Set the engineering and modeling practices used by the team • Own strategy and execution for the applied ML domain, including roadmap, priorities, resourcing, and results • Mentor technically, guide modeling and architecture decisions, review pull requests, and stay current on the codebase and production systems • Partner with senior leadership, Product, Engineering, and Risk to set priorities and deliver on aggressive timelines • Represent the domain in product strategy discussions and help shape product direction • Own fraud detection and identity models across data acquisition, feature engineering, labeling, model training, experimentation, deployment, monitoring, and iteration • Research emerging fraud patterns and build ML capabilities for identity verification and financial risk • Design analyses informing product and business decisions • Drive AI use in the team's work and help define where AI belongs in products

🎯 Requirements

• 10+ years of industry experience applying machine learning or statistics to real-world problems, or 7+ years with a relevant PhD • 6+ years directly managing machine learning or data science teams across two companies or more • Startup experience strongly preferred • Experience leading ML or data science teams in fraud, identity, fintech, banking, financial services, payments, or adjacent risk-focused domains strongly desired, but not strictly required • 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 • Experience writing production-quality Python code and tests • Strong practical ML and applied statistics knowledge • Track record of owning a technical domain and driving it to measurable business impact • Fluent with modern LLMs and AI-assisted development workflows • Sound judgment when working with sensitive data under real information security and data governance constraints • Excellent communication with senior leadership and cross-functional stakeholders • Detail oriented and thoughtful • Legally authorized to work in the United States • Must live in the United States • Technologies: Python 3, PostgreSQL, AWS, XGBoost, scikit-learn, pandas, Elasticsearch, OpenSearch, Neo4j, MLflow, Flyte, and modern LLM tooling

🏖️ Benefits

• 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 • Equity