Experience
A timeline of my professional journey and key achievements.
Co-Founder (Part-time)
GradientCore
Pioneering the future of business efficiency through AI innovation. At GradientCore, we transform complex business challenges into elegant automated solutions, leveraging cutting-edge AI technology to create custom applications that drive real-world impact. From intelligent process automation to advanced AI integrations, we're helping businesses step into their next evolution.
AI Engineer, PD Data Science & Analytics, Data Strategy & Delivery, Data Curation & Integration
Roche
Developing end-to-end AI solutions including AI agents and GenAI applications to accelerate drug discovery, enhance decision-making processes, and expedite the delivery of new treatments to patients.
Data & AI Engineer (Consultant), Computational Sciences and Informatics, Computational Biology Data Insights & Management
Roche
Architected multi-agent AI systems and built scalable data applications for analyzing immunological sequencing data, enabling cross-study immune profiling and clinical insight generation.
Key Achievements
- Architected a multi-agent AI system for orchestrating complex biomedical data workflows—agents collaborate to interpret natural language queries, generate and validate SQL, and extract domain-specific insights from immunological datasets
- Developed a scalable data application for analyzing immune repertoire sequencing data across clinical studies, integrating diverse immunological datasets using AWS-based infrastructure
- Designed and deployed robust ETL pipelines for immune repertoire sequencing, enabling harmonized, cross-study immune profiling and longitudinal analysis
- Built a large language model (LLM)-powered conversational AI interface for natural language querying of immunological databases, significantly enhancing data accessibility for non-technical researchers
- Created dynamic, interactive dashboards for exploring immune response data, facilitating clinical insight generation and hypothesis testing
Research Intern, Medical Affairs, Primary Care - Vaccines
Pfizer
Developed ETL pipelines for epidemiological data analysis, conducted comparative studies on vaccines, and supported Lyme disease research.
Key Achievements
- Built ETL pipelines to process epidemiological datasets (ABCs, NML), enabling real-time analysis of Invasive Pneumococcal Disease (IPD) trends
- Led comparative analytics on IPD serotype coverage across Pfizer and competitor vaccines, delivering insights for market positioning
- Supported data-driven modeling to reveal 5-11x underreporting of Lyme disease in Manitoba, informing vaccine trial design and public health planning
Research Assistant I, Biofluids and Global Health Lab
Department of Bioengineering, McGill University
Optimized a 3D phage-bacteria simulation model using parallel programming to improve performance and scalability for high-dimensional biological data.
Graduate Researcher, Biofluids and Global Health Lab
McGill University
Developed Bayesian machine learning pipelines for calibrating COVID-19 transmission models and evaluating public health interventions in Quebec.
Key Achievements
- Developed a Bayesian machine learning pipeline (ABC-SMC) to calibrate a COVID-19 transmission model for evaluating public health interventions
- Simulated counterfactual vaccination scenarios to assess the epidemiological impact of hesitancy and age-prioritized strategies
- Demonstrated that government interventions were near-optimal and identified a potential 2% hospitalization reduction through targeted refinements
Teaching Assistant, Introduction to Physical, Molecular and Cellular Biology
McGill University
Led computational biology labs, guiding students in applying data analysis methods to molecular transport and cell physiology.
Research Assistant, Epiverse TRACE
Faculty of Medicine, Universidad de los Andes
Developed data packages for Aedes-borne disease transmission analysis and implemented web-scraping pipelines for data integration.
Key Achievements
- Developed data packages to enhance mathematical and statistical tools for understanding Aedes-borne disease transmission
- Conducted data audit and curation of vector-borne disease databases by reviewing health, demographic, and socioeconomic datasets
- Implemented web-scraping pipelines to retrieve and standardize government data, integrating it with local datasets
Undergraduate Researcher, BIOMAC and COLEV labs
Department of Biomedical Engineering, Universidad de los Andes
Designed models for school reopening evaluation and developed COVID-19 forecasting tools to inform public health decisions.
Key Achievements
- Designed models showing partial school reopening (up to 55%) was feasible without raising mortality under strict quarantine
- Engineered predictive tools using statistical, ML, and deep learning models to forecast COVID-19 spread in Colombia
- Built back-end systems for web applications using mathematical models to inform the public about COVID-19 risks
Undergraduate Researcher, Bionanotechnology and Biomaterials Lab
Universidad de los Andes
Coded multiphase flow models to simulate nanofluids behavior in microfluidic devices to propose pharmaceutical transport systems.
Intern, Neuromodulation Business Unit
Boston Scientific
Oversaw machine learning analysis (XGBoost) to assess the cost-effectiveness of deep brain and spinal cord stimulation therapies.