The setting
is a marketplace and software platform that connects patients, health insurers and independent providers of mental health care across the United States. is the software therapists and psychiatric prescribers use to accept insurance and run their practice.
Fast remittance data needs a human decision before it becomes trusted payment information.
Finance needs current payment reporting while Data Engineering needs evidence strong enough to hold a pipeline run.
Who you’d be doing this for
“I checked a session twice because the payment changed after I’d already balanced my week.”
Alejandro Lima · Independent Clinical Social Worker · a character in this brief
Runs a solo practice and relies on payment information to reconcile weekly insurance reimbursement.
What is at stake
Payment totals are changing after remittance refreshes, leaving clinicians and internal partners with conflicting records. You must decide what AI can flag automatically and what evidence an engineer must review before data is held.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- early detection vs false holds
- payer variation vs stable data
- downstream report freshness vs settled remittance data
- faster triage vs sustainable alert load
Why Headway
Provider payment and claim-status workflows depend on remittance data that clinicians use to understand their reimbursement.
Written with these in mind
Not your kind of problem? 104 more at Headway, or browse every organization.
Where the organization context comes fromThe postings this brief was written from, and the reporting read before it.
The organization context comes from public information. The situation and characters are written for the brief.
Job postings it was written from
Public descriptions of the work at Headway. They are listed as sources, not as openings. The lines under a posting are its own words, where it meets this brief.
- Staff Data Scientist · no longer posted
- Patient: The Patient group owns how someone finds mental health care on Headway and gets to a provider who fits them: the first visit or phone call, search and matching, booking, and then attending a first appointment and staying in care.
- Payer & Outcomes: The Payer & Outcomes group is responsible for driving best-in-class patient outcomes, building a trustworthy and safe provider network, and delivering value-based care with responsible costs relative to outcomes, both through product work across the company and by working directly with payers.
- Staff Data Infrastructure Engineer · no longer posted
- The Data Platform team is a group of Data Engineers and Data Infrastructure Engineers who build and own our Snowflake data warehouse, data ingestion tooling, AWS infrastructure, CI/CD, and data developer experience.
- Senior Data Engineer (opens in a new tab)
- We partner closely with stakeholders across the company - including Finance, Insurance Systems, Analytics, Product, and Data Science - to ensure that Headway's data platform is scalable, reliable, and secure.
- Senior Staff Data Scientist - Bayesian Experimentation & Causal Inference · no longer posted
- Senior Data Infrastructure Engineer · no longer posted
- Ship small, learn fast: We are building new and novel products and believe that we learn what our users need by quickly shipping and iteration.
- APM tools like DataDog, New Relic, custom alerting/monitoring, data quality & observability tooling
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.
Where similar work appears
Other live briefs connected to this one, and what connects them.
Similar problems at other organizations
Like this one, each of these is about money moving incorrectly, late, or twice.
Duplicate request records can make homeowner demand look larger than it is.
You must decide what AI can flag automatically and what a data engineer must verify before data is held.
Completed jobs can show an invoice while payment remains absent from the owner’s view.
You must decide which investigation work AI can assemble and which judgment engineers must retain.
Billing-query cases lack a single, reviewable payment story, so merchants repeat details while cases move between teams.
You must decide how far the AI carries a case brief between the Salesforce case notes and the SQL payment results, and which explanations the Finance analysts still reconstruct themselves.
The same setting, from another role
Each is set around Headway’s Headway for Providers, written for a different role. They’re separate briefs, not the same scenario.
New clinicians are missing first appointments after completing early onboarding steps.
You must decide whether AI drafts explanations from evidence or only assembles the evidence for PM review.
Virginia clinicians are unsure whether their enrollment records are complete before January 29.
You must decide which readiness signals prompt action without implying payer approval.
Clinicians are receiving lower payments while claim records still look processed.
You must choose which suspected downcodes can move automatically and which need human review.
Other problems with a similar purpose
In each of these, as in this one, the aim is to build machine-learning systems that earn trust by staying explainable where their errors carry real consequences.
An audit found sensitive fields in 7% of shared artifacts that manual review missed, and the classifiers disagree on the edge cases.
You have to build evidence for a production commitment you cannot easily reverse.
One threshold sends very different numbers of drivers into coaching in each region, and reviewers there agree less often.
You have to balance fair treatment of drivers against enough coverage to improve coaching.
Confirmed takeover messages are reaching guests, while legitimate pre-check-in threads are also being blocked.
You must weigh fraud prevention against a guest losing the instructions needed to enter a booked stay.
