Head of Machine Learning - Application Fraud
Full-time
Remote
New York
$230K - $260K
Role highlights
Intake call
Requirements
Pipeline
About this role
What to ask
Interview process
Ideal candidates
Rejected feedback
About SentiLink
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Taylor Crawford
Taylor Crawford
Technical Recruiter
Engaged
Top 35% of hiring managers
Time to first action
43 hours
Reply time
7 days
Active days
7 days
Feedback quality
Clear
Latest announcement
· Aug 18, 2026
There's been a slight change to the role. We're still looking to hire a Head of ML if we can find the right profile, and nothing has changed in terms of the requirements for that search. However, we're now also open to considering lower-level managers, which should broaden the pool a bit.There are a few important caveats for the DS Manager profile:Management experience: Minimum of 3–4 years of direct people management experience. TL experience does not count toward this requirement.Domain + startup experience: They must have relevant domain experience (fintech, fraud, credit risk, underwriting, etc.), as well as startup experience.Comp: The DS Manager role will cap at $250K, compared with $260K for Head of ML.Core profile requirements remain the same: We're still looking for hard STEM degrees, top-tier schools, strong tenure, and substantive applied ML experience. We should still avoid profiles primarily focused on:"Soft" DS: analytics, growth, marketing, experimentation/A/B testing, dashboards, strategic/business insights, etc.GenAI-focused work: RAG, agentic AI, LLMs, GenAI, transformers, neural nets, etc.The biggest change is simply that we now have more flexibility on management level and scope but the technical, academic, domain, and quality bars remain high.
Role highlights
Hiring goal
1 candidate
Recruiter activity
Recruiter activity on this role is high. Over the last 10 days, 4 candidates were submitted to this role, 1 of them was submitted by 1 active primary recruiter.
Why candidates should join
Identity Fraud Is A National Crisis And The Market Is Enormous – Identity theft and synthetic fraud cost the US economy tens of billions of dollars every year, and the problem is getting much more complex to solve, not easier. SentiLink is the first company to go live with the eCBSV and the chosen partner of 13 out of the top 15 US banks including JP Morgan Chase and Citibank.
Unmatched Growth At This Scale – SentiLink has raised $85M from Andreessen Horowitz, Craft Ventures, and other top tier investors. The company hit cash flow positive in 2023 and profitability in 2025 at Series B.
Fully remote across the US
Top 1% Team working on Hard Problems
Able to sponsor most visas
Very Data Science forward company. Naftali Harris - CEO @ Sentilink, previously a Data Scientist at Affirm
Ever-evolving problems (synthetic, first-party, third-party fraud) — never stagnant, never "solved."
High visibility and high impact — projects regularly seen by senior leadership.
True end-to-end ownership: data scientists take models from conception through feature engineering, production code, and deployment. No handoff to an MLE.
This manager owns how the product suite grows and how the team scales; will explicitly be driving direction.
Working with government agencies and fortune 500s. - press link
Suggested outreach
Intake call
Recorded by Parascribe
Company Overview
Sentielink focuses on fraud detection with high visibility and pressure due to being a data science company.
The team builds core fraud models that differentiate the company, making it a key team.
Role Details
Role: Head of ML, similar to a Senior Data Science Manager.
Responsible for managing the application fraud team, building zero to one models, and running the suite of fraud products.
Candidate Requirements
Looking for top 1% individuals with a combination of data science and MLE skills.
Preferred experience in cybersecurity, health tech, or fintech with a strong understanding of fraud models.
Technical and Experience Requirements
Must have 4-5 years of management experience and have scaled products and teams.
Needs to have hands-on coding ability, though they won’t code regularly, and must pass a technical coding exercise.
Education Requirements
Preferred Master's or PhD in a relevant STEM field, but exceptional Bachelor's profiles considered.
Compensation and Logistics
Salary range: $210k to $250k, with potential flexibility up to $260k for the right candidate.
Remote role in the US, with visa sponsorship available in most cases.
Interview Process
Four rounds: recruiter call, hiring manager interview, technical (coding and case study), and leadership round.
Leadership round involves the head of people, CTO, and CEO.
Ideal Candidate Profile
Combination of machine learning engineering and data science, capable of writing production code.
Strong communicator, able to articulate complex information effectively.
Team Culture and Expectations
High-caliber, select team with high expectations and visibility.
Autonomy in growing products and scaling teams, with a strong focus on technical and problem-solving capabilities.
Requirements
By tier
By category
Hear the hiring manager outline the qualifications needed
07:53
Calibrate a candidate in Parabot. to see how well they fit the requirements.
Dealbreaker
Work experience
3+ years managing data science/ML teams of 5-15 people (must have managed people who are building ML products core to the business)
Updated
Hard skills
End-to-end ML: feature engineering, model training, productionalization, monitoring
Miscellaneous
Should have average tenure of >2 years at each company (hard filter on people who frequently are moving jobs)
Baseline
Seniority
7 - 15 years of experience in applied ML/data science, building production models in fintech, cybersecurity, or other high stakes domains
Work experience
Proven track record of solving complex / high profile business problems with DS / ML solutions. (Scaled a product suite of ML models, not just one model)
Experience at a fast growing startup (20-500 people)
Updated
Education
Bachelors, Masters, or PhD in a STEM field (math, stats, CS, physics, engineering) - target top universities
Updated
Hard skills
Strong software engineering abilities in Python
Soft skills
Experience in communicating progress + outcomes to senior management / stakeholders
Nice-to-have
Work experience
Shows great slope and career progression
Hard skills
Domain experience in fraud, identity verification, or financial risk
Traits to avoid
IMPORTANT – avoid backgrounds recently focused on LLMs, gen AI, RAG, or agentic AI. It's not what we're looking for in this hire
Updated
No formal people management (TL ≠ manager)
Product/business analytics data scientists
Updated
Only large-company experience with narrow scope (e.g., manager at Meta/Google with no product ownership)
Pipeline
Your candidates
Analytics
Inbox
1candidate
Recruiter Screen - 20 minutes
2candidates
Hiring Manager Screen with Ido, Director of Data Science - 30 minutes
1candidate
Technical Interview - 2x 1 hour
0candidates
Virtual Onsite Panel (3 interviewers) - 1 hour
0candidates
Leadership Round + References - 1 hour
0candidates
Offer
1candidate
Hired
0candidates
Recent Activity
Changed status of Candidate #3 from Submitted to First round
3 days ago
Changed status of Candidate #2 from Submitted to First round
3 days ago
Changed status of Candidate #1 from Submitted to Rejected
3 days ago
Check in call with Paraform operations team
6 days ago
About this role
Hear the hiring manager describe the role and its responsibilities
02:53
As Head of Machine Learning, Application Fraud at SentiLink, you will directly manage a team of talented data scientists that build our suite of products: models that identify fraudsters and also advance our growing suite of products in financial risk. In this capacity, you will be relied upon to be a technical leader, mentor, and owner of your respective domain, both for your own team and the organization. Utilize your abilities as a strong data scientist to mentor your team, probe their understanding and challenge them to do their best work. Your team will often work on projects with high visibility and impact that require deep domain understanding, critical thinking and strong technical abilities.
This role requires managing full-stack data scientists, whose work involves model development, analysis, and writing production code. You yourself should share that same skillset and be interested in having end-to-end ownership in a fast-moving environment where deep domain understanding drives development and unusual insights drive our competitive advantage rather than optimization of new machine learning methodologies.
Responsibilities:
Directly manage a team of highly skilled data scientists. Start from 2-3 and grow to 5-6.
Act as a technical mentor that can dive deep and provide detailed direction.
Lead planning, resourcing and communications with senior leadership, product and engineering teams.
Develop strong business intuition and guide your team to deliver performant DS solutions on aggressive timelines.
Develop and maintain SentiLink’s fraud detection models through the full model development lifespan: from data acquisition decisions through featurization, focusing labeling resources, model training, experimentation, productionalization, and monitoring.
Research new types of fraud and develop new SentiLink products around identity verification.
Achieve success by researching / developing through iteration, integration of new data sources and inventive feature engineering.
Write production-ready code that can be relied on for real-time decision making by our partners.
Design, perform, and present analyses that will inform data acquisition, product development, risk operations priorities, marketing, and sales efforts.
Role details
Salary
$230K - $260K
Remote work policy
US-based, remote. Option for in-person work in various US cities
Full-time position
Location
New York, San Francisco, South Bay Area, Los Angeles, Boston, Seattle, Texas, Chicago, Washington DC, Denver, Florida
Tech stack
Python 3, PostgreSQL, AWS (EC2, S3, RDS, Redshift), ML Tooling
Equity
Competitive equity
Visa sponsorship details
Open to visa transfers (e.g. OPT, H1B transfers)
Additional visa sponsorship details
Able to sponsor most visas
Hiring Count
Looking to hire 1 candidate for this role
Report to
https://www.linkedin.com/in/ido-sh/
What to ask candidates
What is their current visa status?
Describe a product/model you took end-to-end from conception through deployment that was core to the business. What was the impact? (optional)
What is their salary expectation?
How actively is this candidate exploring new opportunities?
Requirements
By tier
By category
Hear the hiring manager outline the qualifications needed
07:53
Calibrate a candidate in Parabot. to see how well they fit the requirements.
Dealbreaker
Work experience
3+ years managing data science/ML teams of 5-15 people (must have managed people who are building ML products core to the business)
Updated
Hard skills
End-to-end ML: feature engineering, model training, productionalization, monitoring
Miscellaneous
Should have average tenure of >2 years at each company (hard filter on people who frequently are moving jobs)
Baseline
Seniority
7 - 15 years of experience in applied ML/data science, building production models in fintech, cybersecurity, or other high stakes domains
Work experience
Proven track record of solving complex / high profile business problems with DS / ML solutions. (Scaled a product suite of ML models, not just one model)
Experience at a fast growing startup (20-500 people)
Updated
Education
Bachelors, Masters, or PhD in a STEM field (math, stats, CS, physics, engineering) - target top universities
Updated
Hard skills
Strong software engineering abilities in Python
Soft skills
Experience in communicating progress + outcomes to senior management / stakeholders
Nice-to-have
Work experience
Shows great slope and career progression
Hard skills
Domain experience in fraud, identity verification, or financial risk
Traits to avoid
IMPORTANT – avoid backgrounds recently focused on LLMs, gen AI, RAG, or agentic AI. It's not what we're looking for in this hire
Updated
No formal people management (TL ≠ manager)
Product/business analytics data scientists
Updated
Only large-company experience with narrow scope (e.g., manager at Meta/Google with no product ownership)
