Lead the strategy for patient-provider matching using ML-powered ranking systems at Headway.
Posted by employer 2 months ago
First seen on Joblaze 2 months ago
Last verified on the company career page 14 hours ago
Skills & Technologies
AI in the day-to-day
The ranking stack is ML-powered, focusing on learned relevance models and semantic search.
Requirements
Not disclosed in this posting: compensation, work arrangement, visa sponsorship.
Joblaze summary
In this role, the Staff/Principal Product Manager for Ranking at Headway focuses on optimizing the patient-provider matching process, ensuring that users connect with the right mental health professionals. The position requires expertise in machine learning, search algorithms, and marketplace dynamics, as well as a strong background in product management, particularly in ranking and relevance systems. Ideal candidates will have over a decade of experience and a proven ability to navigate complex technical challenges while aligning cross-functional teams around strategic goals. Headway, a Series D company, is committed to transforming mental healthcare accessibility and affordability.
Joblaze insights
Quick facts
From the original posting
One million patients a month ask for a referral to a mental health provider. When they search on Headway, the order of results decides who books and who gives up. You own that order.
The scope is patient–provider matching: the retrieval and ranking systems, the strategy for where they go next, how we think about our data strategy and platform to . Your mission: maximize successful matches. Not clicks. Not bookings. Patients who connect with a provider, stay in care, and get better.
It's the most technical PM seat in the Patient org. The ranking stack is ML-powered, and the roadmap runs deep: learned relevance models, semantic and free-text search, matching built on better evidence of each provider's expertise. You'll set that roadmap and get the executive team behind it, working with a dedicated pod of search and ML engineers and embedded data scientists. This is not a role for someone who needs a well-defined problem. The best candidates have a track record of finding the right problem and convincing the organization to solve it.
Understanding what patients actually need. Patients rarely arrive knowing the right filters. We're building search that lets someone describe what they need in their own words and still surface the right providers. Semantic search, LLMs, and embeddings are all in play.
Ranking for the relationship, not the click. A ranker that optimizes for conversion alone can win the booking and lose the patient. Rebuild it to optimize for care relationships that last.
Expertise. Every patient should land with a provider who has real experience helping people like them. Getting trustworthy expertise signals into matching, fairly to providers, is an unsolved problem. It's yours.
Marketplace health. Patients need care fast. Providers across the network need to build caseloads. When those two pull in opposite directions, you make the call.
Set the strategy for search, retrieval, and ranking, and get engineering, data science, design, clinical, and the executive team behind it.
Run the experimentation program end to end: hypotheses, objective functions, guardrail metrics, honest readouts. Report the losses too.
Build the data, evaluation, and ML platform foundations that unlock the next two to three years of matching work.
Work daily with ML engineers and data scientists on model objectives, features, and evaluation. You won't write the models. You will be a real thought partner on what they optimize.
Carry ranking's priorities across the company. Ranking touches patient experience, provider growth, clinical quality, and payer partnerships; resolving those competing priorities is the job, not a distraction from it.
Set the bar for technically grounded product work across the PM team.
You have 10+ years of product management experience, including hands-on ownership of search, ranking, recommendations, or marketplace matching at scale.
You've shipped ML ranking or relevance systems to real users. You can discuss objective function design, feature development, and offline vs. online evaluation from experience.
You can hold an architecture conversation with ML engineers, challenge modeling assumptions, and make informed build-vs-buy calls.
You've lived marketplace trade-offs and understand that ranking allocates opportunity across one.
You're fluent in embeddings, semantic search, and LLM-powered product, and you know which of them actually ships value.
You've presented strategy to executives and held your ground on the hard questions.
You're motivated by Headway's mission of making mental healthcare accessible and affordable.
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