Nihal Pai

MMM Joint Degree Candidate · MBA + MS Design Innovation

B.S. Computer Science · Magna Cum Laude

Genomics institute delivering global research and clinical sequencing; 3M+ samples for 1,400+ groups in 50+ countries.

• Led development of core Python and Airflow features for high-throughput genomic pipelines supporting 94K+ samples across 5+ clinical and research programs and enabling patient-sample delivery for 7+ government, industry, and academic partners; recognized as the repository's top contributor.
• Collaborated with clinical geneticists, laboratory teams, and engineers to resolve sample-review bottlenecks, translating user needs into requirements and technical scopes that accelerated downstream handoffs and reduced the risk of reporting delays.
• Partnered with technical leads on scoping and architecture reviews across 5+ platform initiatives, authoring select designs and evaluating maintainability, scalability, and implementation-speed trade-offs.
• Drove production releases for genomics pipelines, coordinating feature-risk assessment, testing, infrastructure changes, security validation, deployment monitoring, and cross-team remediation.
• Defined PostgreSQL data models and production quality signals spanning sample, laboratory, and pipeline activity, improving end-to-end traceability and detecting failures before they delayed downstream clinical processing.

• Developed and operated a production system moving COVID-19 sample and result data from intake to reporting for hospitals, schools, and public-health partners, supporting a broader operation that delivered 36M+ tests in 3.3 years and 5% of U.S. tests at peak.
• Productized onboarding, validation, and data-exchange capabilities to automate a 9-step customer integration process, standardizing data contracts and reducing configuration back-and-forth across genomics programs.
• Developed a data pipeline for an NIH-funded study of 10 chronic medical conditions, automating result processing, quality checks, and clinical reporting for 155K+ genetic risk results across 25K+ participants and 10 clinical sites; co-authored the resulting Nature Medicine publication.

Health-tech company building digital patient-engagement platforms for pharmaceutical clients and nurse care teams.

• Converted complex nurse-care protocols into configurable full-stack product experiences for onboarding, communication, and medication management, supporting 4+ pharmaceutical clients and 4+ medication programs.

Digital health platform unifying patient communication and management operations for healthcare practices.

• Designed two core health-analytics and patient-onboarding experiences during Techstars' UnitedHealthcare accelerator, creating a reusable visual system for communicating complex clinical data to care teams.

Singapore

• Built machine-learning forecasting models for automotive sales using Python, Pandas, Prophet, and AWS SageMaker, analyzing historical demand patterns to support commercial planning.
• Analyzed used-car sales data and developed geographic visualizations using Python, Matplotlib, R, and Leaflet to surface product and regional market patterns.