Biostatistician / Data Scientist (Contract, Part-Time - Research Collaboration)

About this role

About Total Life
Total Life is a behavioral health telehealth practice serving older adults, with 200+ licensed clinicians across 49 states. Most of our patients reach us through Medicare and commercial insurance, and a large share are aging adults who fall outside traditional VA and specialty mental-health infrastructure. Because we deliver care remotely and repeatedly over time, we hold something unusual: dense, longitudinal behavioral-health measurement on an older-adult population that is rarely captured at this scale or cadence.

The research
We are building a research program around a simple but underexplored idea: most suicide-risk detection treats risk as a fixed state, measured once. We want to test whether the *dynamics* already present in routine care — how symptom scores move over time, and how patients engage with care as those symptoms change — carry information about near-term risk that standard one-time screening misses. The initial project is a retrospective study on fully de-identified data (no new data collection, no patient contact), developing and validating a multimodal risk-detection model and quantifying its incremental value over standard screening. This work is being submitted for federal research funding, and the person in this role would be a named co-investigator on that application.

The role
You would lead the statistical thinking behind this work — design and analysis, not just execution. Specifically:

• Serve as co-investigator on a federally funded research application (biosketch and defined effort commitment required)
• Design the statistical and validation plan: power / sample-size analysis for a rare-event outcome, model evaluation, discrimination and calibration, temporal validation
• Advise on analytic methods for de-identified behavioral-health and claims data, including de-identification standards
• Partner with our clinical and engineering teams to guide model development and interpret results resp...