Director, AI and Data Science

Director, AI & Data Science

Triveni Bio is breaking new ground in the identification of novel disease targets – working at the convergence of human genetics, best-in-class antibody design, and precision medicine. We are pioneering a Mendelian genetics-informed precision medicine approach to develop functional antibodies for the treatment of immunological and inflammatory (I&I) disorders. Our lead antibody program (TRIV-509) targets kallikreins 5 and 7 (KLK5/7) to directly impact skin barrier function, inflammation, and itch – providing a meaningful and much needed potential treatment option for patients with atopic dermatitis and other barrier disorders.

In all the work we do, we adhere to our core values: patient impact, bold and rigorous science, open collaboration, kindness & respect.

We are looking for a versatile Director, AI & Data Science to join our growing Data Science team. This is a high-visibility role, working closely with the Head of Data Science, that sits at the intersection of enterprise AI enablement and hands-on scientific data analysis. You will spend half of your time engaging cross-functional stakeholders, including Translational Medicine, Biostats, Finance, Business Development, and others, to understand their workflows, identify automation and AI opportunities, and deliver tailored solutions.

The remaining half will be dedicated to hands-on research and clinical data science work, personally running the analyses and building the pipelines.

This is an ideal role for someone who is equally comfortable conducting a stakeholder discovery session as they are writing a Python script, and who thrives in a fast-paced biotech environment where no two weeks look the same.

AI Enablement & Cross-Functional Solutions

• Partner with leaders across multiple functions to gather requirements, understand pain points, and identify high-impact AI/automation use cases
• Translate business requirements into reproducible, script-based solution designs, from automated data workflows to LLM-augmented tools
• Prototype, evaluate, build, and deploy AI-assisted solutions as reproducible scripts and pipelines (not just chatbot interactions), tailored to each function's needs
• Develop and maintain an AI use-case roadmap tracking opportunities, feasibility, and impact across the organization
• Support the company-wide rollout of AI collaboration tools (e.g., Google Gemini, ChatGPT, Claude), including training, best practices, and governance
• Create documentation, SOPs, and lightweight training materials to drive adoption and self-sufficiency within each function
• Track adoption metrics and user feedback to iterate on deployed solutions

Human Genetics, Bioinformatics & Translational Research

• Lead human genetics analyses, including human genetics scoring, variant interpretation, and genotype-phenotype association studies
• Drive real-world evidence (RWE) data curation and analysis using platforms such as TriNetX, All of Us, and UK Biobank
• Build and own bioinformatics workflows (e.g., sequencing data analysis, proteomics/transcriptomics expression profiling, biomarker analysis)
• Partner with Biometrics and Clinical Development to support clinical and biomarker data analyses across ongoing trials (e.g., exploratory, biomarker, and post-hoc analyses)
• Partner with the research team to deliver data visualization, reporting, and exploratory analyses that inform target and program decisions

• Master's degree or above in Data Science, Computer Science, Bioinformatics, Computational Biology, or a related quantitative field with 5 years of relevant experience in data science, analytics, or AI/ML, preferably in biotech, pharma, or healthcare
• Proficient in Python and/or R
• Hands-on experience with LLMs / generative AI tools (prompt engineering, API integration, or building AI-powered workflows) and experience with enterprise AI platforms
• Familiarity with cloud platforms (AWS, GCP) and data infrastructure and high dimensional data
• Exposure to bioinformatics tools and pipelines (e.g., Omics analysis, variant calling) and statistical human genetics methods (e.g., GWAS/PheWAS, fine-mapping, colocalization,…