Analyst, Feasibility AI Apps

Tempus AI•Chicago, IL

2d•$70,000 - $105,000

About The Position

The Analyst, Feasibility AI Apps supports feasibility analysis and core analytics infrastructure. The role translates clinical, genetic, and business requirements into technical scripts and queries, then turns results into clear visualizations and insights for stakeholders and client presentations. Working with senior team members and business development, translational research, and clinical teams, the analyst helps operationalize patient cohort definitions and analyze multi-source datasets, including EMR, genetic, and laboratory data, as well as relevant secondary sources. The analyst also helps improve feasibility workflows, integrate and harmonize data using SQL and dbt, maintain data quality, and update dashboards and visualizations such as those in Looker. The position involves developing working knowledge of oncology and clinical genomics concepts so they can be accurately translated into technical deliverables. During pre-sale downtime, the role provides secondary support for core analytics infrastructure.

Requirements

Bachelor’s degree in an analytical, computational, or healthcare-related field, such as Bioinformatics, Data Science, Public Health, Biology, Computer Science, or Biomedical Engineering.
Two to three years of relevant experience in healthcare analytics, clinical data, or related fields, or a master’s degree in a related field.
Proficiency in SQL or an equivalent query language and experience with data transformation tools such as dbt.
Experience with data visualization tools such as Looker or Tableau.
Ability to interpret and implement complex clinical or genetic cohort definitions in code.
Excellent interpersonal, written, and verbal communication skills, including the ability to explain technical concepts to non-technical stakeholders.
Strong organizational skills and attention to detail.
Ability to work efficiently under time constraints and manage multiple priorities.
Ability to collaborate across technical, clinical, and operations teams, along with strong problem-solving skills.
Proficiency with Google Suite and Excel.
Willingness to work in an agile, fast-paced, expanding team environment.
Experience working with EMR, clinical, or claims data.

Nice To Haves

Familiarity with oncology, clinical trial design, or rare disease research.
Experience with R or Python for data analysis.
Experience leveraging agentic tooling to rapidly prototype solutions related to this work.
Experience working with genetic or molecular data, such as NGS or variant annotation.

Responsibilities

Translate clinical, genetic, and business requirements into technical scripts and queries using SQL or similar tools, and present results as actionable visualizations and insights.
Work with business development, translational research, and clinical teams to understand cohort eligibility criteria and help translate them into precise data extraction logic.
Analyze multi-source EMR, genetic, and laboratory datasets, along with secondary sources such as peer-reviewed publications, to identify eligible patient populations with senior team support.
Generate analytics including attrition funnels, geographic distributions, and provider or institution metrics for client presentations.
Support workflow optimization by maintaining and documenting process improvements, supporting automated pipelines, and helping ensure handoffs for post-sale algorithm development.
Use SQL and dbt to help combine clinical, genetic, and operational data for patient identification and analytics.
Follow established quality-assurance processes to identify and correct errors, inconsistencies, and gaps in data and feasibility outputs.
Maintain and improve data visualizations and dashboards, such as Looker dashboards, for feasibility or post-sale reporting and business development.
Develop working knowledge of relevant oncology and clinical genomics disease areas to support accurate translation of clinical concepts into technical deliverables.
Provide secondary support for core analytics infrastructure during pre-sale downtime.