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.
The access change preserves data availability only if note-derived datasets can be trusted.
Analytics and machine learning need continuity, while clinical documentation requires verified lineage and permissions.
Who you’d be doing this for
“I spend enough time documenting care already, I need to know those notes aren’t showing up somewhere unexpected.”
Claudio Greco · Independent Mental Health Clinician · a character in this brief
Uses clinical documentation tools while relying on the platform to handle sensitive practice data responsibly.
What is at stake
Note-derived fields have appeared in datasets with conflicting lineage records. You must weigh uninterrupted internal data access against verified boundaries for sensitive clinical documentation.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- data availability vs. clinical-data boundaries
- fast containment vs. verified granularity
- machine-generated lineage vs. human validation
- model continuity vs. governed inputs
Why Headway
Provider documentation supports clinical workflows, so uncertain access to note-derived data puts clinician trust and internal decision-making on the same path.
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
- The Provider group owns that relationship and the trust it depends on, because we believe a clinician who feels supported can better help their patients.
- 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.
- Staff Data Infrastructure Engineer · no longer posted
- You'll serve as a technical anchor across our Data Analytics & Engineering, Product Engineering, and Machine Learning teams.
- Agentic Data Engineering (automating data infrastructure tasks, auditing permissions, optimized CI)
- Senior Data Engineer (opens in a new tab)
- Help shape how Headway employees interact with our data through AI-powered tools, ensuring the underlying data layer is reliable enough to power accurate, trustworthy answers
- Senior Staff Data Scientist - Bayesian Experimentation & Causal Inference · no longer posted
- As a Senior Staff Data Scientist, Bayesian Experimentation and Causal Inference , you will be the company-wide owner of how Headway learns from data, especially when the stakes are high and the signal is noisy.
- 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.
- You’ll work closely with our Data Analytics & Engineering teams, Product Engineering, and Machine Learning teams to support analytics needs as we grow in traffic, data, and scale.
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.
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 engineer data foundations precise enough that the evidence they produce stays credible long after the system changes.
Retailer identifiers and derived features still sit past the retention window in several warehouse and serving copies.
You have to balance privacy risk against deleting records that orders and support still need.
2.7% of encounter records disagree about completion after amendments, and 11% carry classification fields nobody has verified.
You decide what counts as final, because contract baselines will follow your definitions.
A dry run shows 4% of historical transitions map differently under the new model, which changes time-in-stage and funnel reports.
You have to decide which checks must pass before the old contracts are retired.
