Data Scientist II, Outcomes Research
Tempus AI · 4 Locations · US
Clinical Data Science
–
Not yet analysed
Demand68
Career growth70
Healthcare fit52
Remote potential55
5-year outlook
Health-tech data science roles in real-world evidence tend to pay competitively within the broader data science market, with growth tied to pharma and payer demand for outcomes evidence. Exact figures cannot be assessed without the posting's compensation details.
Clinical license
Not required
Applicant reach
narrow
Seniority
mid
Employment type
–
Date found
2026-10-03
Intelligence details
Flexible work details
- Eligible countries
- United States
Eligibility, licensing, and location restrictions can change. Always confirm details on the source posting before applying.
Why this is worth applying to
Worth considering only if you already have demonstrable quantitative and programming experience (or a biostatistics/epidemiology/MPH-adjacent background) plus a portfolio of healthcare data work. Clinicians without analytics evidence will likely be screened out at the 'Data Scientist II' level.
Who should apply
Physicians or pharmacists with an MPH, MS in biostatistics/epidemiology, or health-services research training; clinicians who have run registry, chart-review, or quality-improvement datasets and can code in R, Python, or SQL; IMGs with strong research and data publications seeking a health-tech analytics track.
CV angle
Reframe real clinical work as data work: quantify registry or chart-review cohorts you built, QI datasets you analyzed, study designs you contributed to, and any statistical software used. Lead with measurable analytical outputs (sample sizes, models, publications, dashboards) and place clinical domain expertise as the differentiator for oncology/real-world evidence interpretation — never claim analytics experience you do not have.
Skills to highlight
- Real-world evidence and outcomes research methods
- Statistical analysis in R, SAS, or Python
- SQL and large healthcare/claims dataset handling
- Epidemiology and observational study design
- Oncology or specialty clinical domain knowledge
- Data visualization and stakeholder reporting
- Cross-functional collaboration with clinical and commercial teams
Role summary
A mid-level data science position at Tempus AI focused on outcomes research — generating real-world evidence and analytics from clinical and genomic datasets to support treatment-outcome and value questions. The posting contains no detail beyond the title and company, so responsibilities and requirements are inferred from the role type.
We publish our own intelligence summary, never the full job description. Always confirm details with the employer when you apply.