Director, AI and Data Science

COMPANY INTRODUCTION

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.

SUMMARY

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.

RESPONSIBILITIES

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

EXPERIENCE & QUALIFICATIONS

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, Mendelian randomization, and rare-variant/burden testing)

Experience building internal tools or dashboards (e.g., Streamlit, R-Shiny, web apps)

Strong interpersonal skills; comfortable working with diverse teams across functions (Finance, Clinical, BD, Research) and engaging stakeholders at all levels, from bench scientists to senior leaders

Self-starter mindset: Comfortable with ambiguity; able to independently scope, prioritize, and deliver in a lean team environment

Understanding of AI governance, data privacy, and safe AI practices in regulated industries desired