# Reach Velocity — Head of Machine Learning / Data Science Manager | Applied ML

- Generated: 2026-08-24 09:46:11 AM EDT
- Original/canonical posting: https://www.linkedin.com/jobs/view/4455871471/
- Posted: 42 minutes ago at capture time
- Applicants: Be among the first 25 applicants
- Work model/location: Fully remote within the United States
- Employment type: Full-time
- Compensation: $250,000-$260,000 base plus equity
- Travel: Not disclosed
- Positioning track: Technical manager
- Fit outcome: FAIL — 58%

## Direct-match strengths

Direct people leadership, Python, production ML, forecasting, feature/data pipelines, deployment, monitoring, and high-growth startup experience.

## Hard or material gaps

Hard domain and mandate gaps: fintech, fraud, credit-risk, or underwriting experience is required, and the role explicitly centers classical applied-ML products rather than GenAI/LLMs. Keith has transferable financial-services exposure and production forecasting, but not the required fraud/risk product record.

## Evidence map

1. Applied ML leadership — 2/3: Direct forecasting, predictive analytics, optimization, SageMaker, TensorFlow, and production ML evidence.
2. Direct people management — 3/3: Led teams of 7, 11, 24, and 25 while remaining hands-on.
3. Python and production engineering — 3/3: Strong hands-on Python, APIs, cloud, CI/CD, deployment, and monitoring.
4. Multiple classical-ML products — 2/3: Several predictive/optimization systems, but recent work is primarily GenAI and platform-oriented.
5. Fintech/fraud/credit-risk/underwriting — 0/3: Required direct domain/product experience is unsupported.
6. High-growth startup environment — 3/3: Direct IDW, SuccessKPI, Ingenuity, and other startup evidence.

## Full normalized job description

Head of Machine Learning / Data Science Manager | Applied ML
Remote – USA | $250k–$260k + Equity
I’m working with a high-growth US technology company building machine learning products used to solve complex, real-world fraud and financial risk problems at significant scale.
This is a leadership role for someone who is still deeply technical. You’ll manage and grow a team of Data Scientists while owning the development of production ML products end-to-end: data, feature engineering, model development, productionisation, deployment and monitoring.
What we’re looking for:
7–15 years in applied ML / Data Science
3+ years of direct people management
Strong Python and production engineering skills
Experience building and scaling multiple production ML models
Fintech, fraud, credit risk, underwriting or closely related domain experience
Experience within a high-growth startup environment
Strong STEM degree, ideally from a top-tier university
Strong career stability and progression
This is
not
an analytics-focused DS role and
not a GenAI/LLM position
. We’re looking for leaders with substantive experience building classical/applied ML products that directly drive business decisions.
Why consider it?
Fully remote across the US, significant technical ownership, high visibility with senior leadership, strong equity and the opportunity to build and scale both the ML product suite and the team.
Visa transfers/sponsorship can be considered. (Must be based in the US)

## Artifact metadata

- Resume and cover letter: Not generated under the fit gate.
- Google Drive used: No