Data Scientist

Direct answer: Tempus is a fast‑growing, Chicago‑headquartered health‑tech company (now public as Tempus AI, NASDAQ: TEM) that hires data scientists across genomics, clinical and imaging data; for a remote Illinois‑based Data Scientist role you should emphasize strong ML/biostatistics skills, clinical/omics domain knowledge, production data‑engineering experience, and examples of applied healthcare projects while following Tempus’s online application and interview stages (resume → recruiter screen → technical interview(s) → take‑home or case task → onsite/virtual final loop). Company intelligence (concise facts you can cite during interviews)

• Company history, size, and industry position: Tempus was founded in 2015 by Eric Lefkofsky to bring AI to clinical care at scale and has grown into a major health‑tech firm focused on genomics, clinical data and AI‑driven insights; it is publicly traded as Tempus AI (NASDAQ: TEM).
• Recent news, growth and strategic directions: Tempus has been scaling both genomics testing and its data/services business, reporting strong revenue growth in 2024 driven by genomics and expanding its data products and pharma collaborations to commercialize de‑identified datasets and analytics for life‑sciences partners.
• Company culture and work environment: Tempus positions itself as mission‑driven (precision medicine, data‑driven clinical decisions) and hires across AI/ML, computational biology and software teams in a tech‑startup meets healthcare environment in Chicago with R&D and product emphasis.
• Values, mission and what they stand for: The stated goal is to personalize treatment using molecular and clinical data and to apply AI to improve clinical decision making and clinical trial matching.
• Office locations and hybrid/remote policies: Headquarters and major presence in Chicago (River North office) with many remote roles and teams distributed across the U.S.; specific roles (like the Illinois
• Remote posting) may be fully remote but report to regional/legal locations as stated on the job posting. Program deep dive
• what this Data Scientist role likely entails (from Tempus hiring patterns and job listings)
• Program structure and timeline: Tempus hiring for Data Scientist II / mid‑level roles typically follows a standard lifecycle: application → recruiter screen (1–2 weeks) → technical phone/video interview(s) (1–2 weeks) → coding/take‑home/data task or case study (1–2 weeks) → final loop (1–2 weeks) → offer; total time commonly 3–6 weeks but can vary with scheduling and role urgency.
• Skills and competencies they look for: strong statistical modeling and ML (supervised and unsupervised), Python (pandas, scikit‑learn, PyTorch/TensorFlow), SQL, experience with genomics/bioinformatics or clinical real‑world evidence (RWE) data, data‑engineering/ETL skills, experience deploying models or working with cloud platforms, and ability to interpret clinical impact of models.
• Daily responsibilities and learning opportunities: building predictive models on multimodal data (genomics, EHR, imaging), feature engineering for clinical datasets, working with bioinformatics and lab teams to validate models, producing analyses for internal stakeholders and pharma partners, and contributing to data productization and research publications.
• Mentorship and training: mid‑level roles at Tempus generally report into cross‑functional teams with senior data scientists, research scientists and engineering managers; expect paired code reviews, project mentorship, domain onboarding (genomics/clinical data) and opportunities to co‑author internal/external research.
• Career progression after completion: mid‑level Data Scientist → Senior Data Scientist → Staff/Principal Data Scientist or technical track into Research Scientist roles or managerial track into Data Science Lead / Engineering Manager; movement into product or clinical affairs (RWE, Trials) is common given Tempus’s business lines. Application success guide
• practical steps and materials
• Exact application requirements and deadlines: apply via Tempus jobs page or the posted RemoteRocketShip listing; typical required materials are resume, cover letter (optional but recommended), and answers to screening questions; Tempus postings rarely list hard deadlines
• applications are reviewed on a rolling basis until the role is filled so apply early.
• Step‑by‑step application process:

1. Submit tailored resume emphasizing healthcare/omics projects and production ML experience (via the job URL or Tempus careers site).
2. Recruiter screen (culture fit, basic qualifications).
3. Technical interview(s): coding, statistics/ML, system design for data pipelines, and clinical/biotech domain questions.
4. Take‑home assignment or case study (data analysis or model building on a provided dataset) or live pair‑programming.
5. Final loop with cross‑functional stakeholders (product, clinical, manager).

• Common interview questions for this role/company (prepare concise, clinical‑minded answers):
• Explain a predictive model you built end‑to‑end: problem, data cleaning, feature engineering, model choice, evaluation, deployment, and clinical impact.
• How would you handle missingness and bias in EHR data? (expect specifics: imputation strategies, sensitivity analyses, cohort definition).
• Describe an ML problem in genomics you worked on or how you’d model variant impact / tumor sequencing signals.
• Coding/SQL puzzle: write a query to join tables and produce cohort metrics or implement a function to compute evaluation metrics.
• Assessment centers or case studies: Tempus commonly uses take‑home data tasks or project‑style case studies for data roles (real‑world datasets, model evaluation and a write‑up) or live coding/data analysis sessions; plan for 8–24 hours of work on take‑homes.
• What makes a standout candidate: demonstrable applied projects in healthcare (publications, GitHub repos, Kaggle), clear understanding of clinical relevance and regulatory considerations, strong production skills (ETL, CI/CD, cloud), and communication skills that translate technical work to clinicians and partners. Insider tips (practical, company‑specific)
• Interview prep tips and what they value: emphasize clinical impact (how your model improves decisions), data provenance and QA, reproducibility, and examples where model deployment changed outcomes or workflow; show domain humility and awareness of healthcare regulatory/ethical constraints.
• Technical vs soft skills priorities: technical competency (ML, stats, coding, data engineering) is required; however, soft skills
• cross‑disciplinary communication, translating technical results for clinicians/pharma, and ability to prioritize product impact
• are equally critical at Tempus.
• Industry knowledge to demonstrate: familiarity with genomics testing (NGS, liquid biopsy concepts), RWE and EHR structure, clinical trial matching concepts, and privacy/PHI handling best practices (de‑identification, HIPAA awareness).
• Questions to ask interviewers (to show genuine interest):
• What is a current data challenge the team is working on and how would this role contribute?
• How are models validated clinically and what is the loop from model to clinician use?
• What career paths have former data scientists on this team taken?
• Red flags to avoid: overstating clinical impact, ignoring data quality/labeling challenges, lack of production/deployment experience, or inability to explain tradeoffs between model performance and clinical utility. Practical information (compensation, benefits, timing)
• Salary/stipend ranges: public salary data varies by level and location; mid‑level Data Scientist roles in Chicago/remote US at comparable health‑tech companies typically range roughly $100k–$170k base + equity and bonus, but verify during offer stage—Tempus is a public company with competitive packages.
• Benefits package details: as a large health‑tech employer, Tempus typically offers standard benefits: health/dental/vision, 401(k), paid time off, parental leave, and equity/RSU grants for full‑time roles—confirm exact details on the offer or job listing.
• Start dates and program duration: standard full‑time positions start based on mutual availability; interview timelines vary and Tempus hires year‑round rather than fixed cohort programs for Data Scientist roles.
• Networking opportunities and alumni connections: Tempus collaborates with major academic centers and pharma; employees often publish and present at conferences and have opportunities to work with partners (Mayo Clinic, Northwestern, pharma) offering strong networking and career mobility. Actionable checklist for applicants (what to do this week)
• Tailor your resume to highlight: end‑to‑end ML projects, production deployment, SQL and Python skills, domain projects in genomics/EHR or RWE, and any publications or collaborations with clinicians.
• Prepare two concise STAR stories: one technical (model built and deployed) and one cross‑functional (worked with clinicians/product to deliver impact).
• Build or polish a portfolio item (GitHub or Kaggle) that demonstrates working with clinical‑style datasets (de‑identified), clear notebooks, and reproducible results.
• Practice likely interview tasks: SQL joins/cohort queries, Python data cleaning, model evaluation metrics for imbalanced clinical data, and whiteboard explanations of model choices.
• Draft 3 tailored questions for interviewers that show product and clinical curiosity (see suggested questions above). Limitations and sources
• The above synthesis is based on Tempus company profiles, investing reports, and Tempus job listing patterns; specific hiring steps and compensation vary by role and over time—confirm details on the Tempus careers page or the specific job posting during application. If you want, I can:
• Draft a tailored resume bullet set and cover letter for this Tempus Data Scientist posting.
• Prepare 4–6 mock interview questions with model answers and a practice take‑home scoring rubric.