Analyst, Feasibility AI Apps

What you'll do

• Act as a technical executor for feasibility analysis and secondary support core analytics infrastructure during pre-sale downtime: Comprehend and assist in translating complex clinical, genetic, and business requirements (e.g., cohort eligibility, patient journey analysis) & execute established solutions to generate actionable technical scripts and queries using SQL or similar tools and translate outputs into digestible data visualizations and insights.
• Interpret and operationalize clinical cohort definitions: Work closely with business development, translational research, and clinical teams to understand nuanced eligibility criteria (e.g., rare genetic variants, disease subtypes) and assist senior team members in translating them into precise data extraction logic.
• Extract and summarize patient data: Leverage large, multi-source datasets, including EMR, genetic, and laboratory data, alongside secondary sources such as peer-reviewed publications to identify eligible patient populations with the support of senior team members. Generate comprehensive analytics and insights, such as attrition funnels, geographic distribution, and provider/institution metrics, to support client presentations.
• Support the optimization of feasibility workflows: Help maintain and document improved process efficiency by supporting automated pipelines and ensuring a seamless handoff for post-sale algorithm development.
• Contribute to integrating and harmonizing diverse data sources: Use SQL and dbt (data build tool) to help combine clinical, genetic, and operational data, supporting robust patient identification and analytics.
• Support Data Quality: Follow established QA processes to identify and correct errors, inconsistencies, or gaps in data and feasibility outputs.
• Assist in dashboard maintenance: Help update and enhance/build data visualizations and dashboards (e.g., in Looker) to streamline feasibility reporting (or post sale reporting needs) and support business development.
• Build an oncology and clinical genomics knowledge base: Develop a working knowledge of relevant disease areas (e.g., solid tumors, hematologic malignancies, rare genetic variants) to ensure accurate support of the translation of clinical concepts into technical deliverables.

Required Knowledge and Skills

• Bachelor's degree in an analytical, computational, or healthcare-related field (e.g., Bioinformatics, Data Science, Public Health, Biology, Computer Science, Biomedical Engineering)
• 2-3 years of relevant work experience in healthcare analytics, clinical data, or related fields OR masters in related field.
• Proficiency in SQL (or equivalent query language) and experience with data transformation tools (e.g., dbt)
• Experience with data visualization tools (e.g., Looker, Tableau)
• Demonstrated ability to interpret and implement complex clinical/genetic cohort definitions in code
• Excellent interpersonal, written, and verbal communication skills, with the ability to explain technical concepts to non-technical stakeholders
• Strong organizational skills and outstanding attention to detail
• Ability to work efficiently under time constraints and manage multiple priorities
• Demonstrated ability to collaborate effectively across technical, clinical, and operations teams, coupled with strong problem-solving skills.
• Proficient in Google Suite (Gmail, Google Docs, Sheets, etc.) and Excel
• Willingness to work in an agile, fast-paced, and 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 for rapid prototyping solutions regarding this work
• Experience working with genetic or molecular data (e.g., NGS, variant annotation)