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Open nowPosted yesterday

Senior Scientist, Computational Biology, Life Science Strategy

Tempus AI143 open roles

Where
Chicago
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Your applicationOpen nowSenior Scientist, Computational Biology, Life Science StrategyTempus AI · Chicago

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The clock on this job

Early applications get read.

12.8% of postings close within 7 days. Measured by our own scanner across the market. Tempus AI postings stay open a median of 51 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.5%3 days
  3. 12.8%7 days
  4. 20.6%14 days
  5. 35.6%30 days
  6. 58.4%60 days
This job: posted yesterday

Tempus AI median: 51 days open

The posting

Passionate about precision medicine and advancing the healthcare industry?

Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.

Senior Scientist, Computational Biology, Life Science Strategy

About Tempus

Tempus is advancing precision medicine through the power of artificial intelligence and the world’s largest library of clinical and molecular data. Our mission is to enable real-time, data-driven decision-making to improve patient outcomes and accelerate drug development.

About the Role

We are seeking an experienced Senior Scientist to join the Computational Biology, Life Science Strategy team. This group operates at the intersection of biological data science and AI, supporting high-impact collaborations with major pharmaceutical partners and our life science strategy. The ideal candidate will demonstrate strong scientific leadership, deep expertise in computational biology including AI tools, and a proven track record of success. The role focuses on integrating large-scale molecular and clinical datasets, generating actionable insights for drug discovery and development, and building next-generation research tools that enhance the impact and efficiency of our work.

Key Responsibilities

  • Life Science Strategy: Partner with internal and external stakeholders including life science strategy, market intelligence and pharma R&D to deliver high impact data insights that reflect the current biopharma landscape including around biomarker and clinical development. Independently translate rough concepts into well thought out analysis plans.
  • Computational Analysis & Insight Generation: Execute robust, reproducible analyses integrating genomic, transcriptomic, imaging, and clinical data. Apply appropriate statistical and computational best practices to derive insights related to clinical trial design, patient selection, treatment response, resistance mechanisms, and disease biology.
  • AI & LLM Innovation: Incorporate LLMs and other AI tools into day-to-day workflows to automate, streamline and accelerate code development, discovery, documentation, and review. Design and prototype agentic workflows integrating foundation models and LLMs to power new insights and predictive models.
  • Method Development and Platform Contribution: Evaluate, adapt, and implement new methods for the analysis of real-world, clinical, and ’omic datasets (e.g., survival analysis, causal inference, multi-modal integration). Contribute to reusable code, internal packages, and best practices that can be applied across multiple collaborations and programs.
  • Cross-Functional Collaboration: Work closely with colleagues in Research, Clinical, Data Science, and Engineering to refine analyses, build scalable solutions, and align scientific efforts with platform and product roadmaps.
  • Scientific Communication: Communicate complex methods and results clearly to both technical and non-technical stakeholders. Prepare and present internal reports, external-facing deliverables, and, where appropriate, manuscripts or conference materials that demonstrate the impact of Tempus data and technologies on partner programs.
  • Leadership: demonstrate strong project-level leadership, to ensure the delivery of high-quality results in an efficient and effective manner. This includes setting clear priorities, coordinating resources, and proactively identifying and addressing potential obstacles to ensure that project milestones and deadlines are consistently met. The ideal candidate will foster a collaborative environment, provide mentorship to junior scientists on project teams, and uphold a standard of excellence in project execution and delivery.

Qualifications

  • Education:
  • PhD in Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or a related field (or Masters degree with 3+ years of relevant experience).
  • Plus an additional 2+ years of relevant industry or post-doctoral experience.
  • Track record of success: proven in peer reviewed publications.
  • Technical ProficiencyProficiency in R and/or Python, including experience with common computational biology and scientific computing libraries. Proficiency applying machine learning, LLM-based coding assistants (e.g., Copilot, Cursor) and agentic frameworks for biological/clinical research. Adherence to good software engineering practices (version control, modular code, documentation). Experience working with SQL and large relational databases. Strong grounding in statistics and data analysis, including study design considerations and interpretation of real-world clinical data. Proficiency in providing quality code review and QA/QC for the work of others.
  • Scientific Knowledge: Strong understanding of cancer biology, immunology, or human disease mechanisms, with the ability to interpret and effectively summarize scientific findings.
  • Data Expertise: Demonstrated experience analyzing large-scale biological datasets (e.g., NGS, RNA-seq, other genomics or transcriptomics data), ideally in oncology, immunology, or human disease.
  • Scientific Leadership: Demonstrated project leadership and people mentorship.
  • Soft Skills: Excellent written and verbal communication skills with comfort in client-facing roles. Strong collaborator. Ability to thrive in a fast-paced, dynamic setting.

Preferred Skillsets

  • Generative AI: Practical experience configuring or adapting LLMs, or using related tools/frameworks, to support scientific work.
  • Specialized Modeling: Expertise in one or more of the following: Real-world evidence (RWE), survival analysis, causal inference, network/systems biology, or multi-modal integration.
  • Publication Record: A strong history of peer-reviewed publications or conference presentations.
  • Drug Discovery Context: Understanding of the drug development lifecycle, from target discovery to clinical development.

CHI: $100,000-$160,000 NYC/SF: $115,000-$175,000

The expected salary range above is applicable if the role is performed from California and may vary for other locations (Colorado, Illinois, New York). Actual salary may vary based on qualifications and experience. Tempus offers a full range of benefits, which may include incentive compensation, restricted stock units, medical and other benefits depending on the position.

Additionally, for remote roles open to individuals in unincorporated Los Angeles – including remote roles- Tempus reasonably believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: engaging positively with customers and other employees; accessing confidential information, including intellectual property, trade secrets, and protected health information; and appropriately handling such information in accordance with legal and ethical standards. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.

We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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