Feb 2022 – Feb 2023 · 1 yr · Mumbai, Maharashtra, India

• Built and Led the Global Data Science & Engineering Charter, powering the Digital Transformation of Abbott’s ~20B USD per year HealthTech (Medical Devices) Business, ground up. Managed Site Budget (Capex, Opex).
• Worked with US leadership to create the product strategy and roadmap of its AI verticals for all the business units: Research & Development, Sales and Commercial, and QA, covering the entire spectrum of clinical, commercial and quality oriented AI softwares for Abbott's path-breaking Health technology products.
• Supervised the four verticals, designing, building and deploying AI-based Health-Tech Apps, using Real World Datasets (Medical Claims, EHRs, EMRs, Real-time Patient Monitoring Data, etc.), for various use-cases across Abbott's seven Medical Device Business Functions: Cardiac Rhythm Management, Cardiovascular, Structural Heart, Electrophysiology, Heart Failure, Neuromodulation and Diabetes care.
• Launched two highly impactful products: (1) AI App that shows US facility level monthly Total Market Volume and Abbott Market Share estimates of all medical devices in the Cardiac Rhythm Management (CRM) business unit and (2) AI App that can classify successful and unsuccessful procedures using the device ‘MitraClip’, based on multiple patient-specific and other external features.

Jul 2021 – Jan 2022 · 6 mos · Bengaluru, Karnataka, India

• Led a team of Research Scientists, Data Scientists and Software Engineers (FE, BE), working on the innovation, full-stack development and deployment of massively scalable AI pipelines in Kubernetes, for various real-world Healthcare R & D problems - Drug Target Discovery and Clinical Development, Electronic Medical Record (EMR/EHR) based Real-World Evidence generation, Multi-omics data based clinical insights generation for patients suffering from various diseases, etc.
• Designed and delivered a diverse portfolio of AI-driven Health-Tech Apps, built on top of a wide array of online databases, leveraging various cutting edge Deep Learning and NLP algorithms.
• Product Tech Stack:
• Backend: Go, Python/PySpark
• Frontend: React, Angular
• Automation - Ansible, Puppet, Terraform
• Pipeline - Jenkins, ArgoCD
• Monitoring - Prometheus, AlertManager, Grafana
• Databases - MongoDB, Redis, MySQL, ElasticSearch
• Pub/Sub - Kafka, RabbitMQ
• Container Orchestration - Kubernetes (K8s)

Aug 2020 – Jul 2021 · 11 mos · Greater Bengaluru Area

• Worked as the Data Science Lead in Early Stage Drug Discovery, within the Translational Biosciences Unit inside R & D.
• Built Digital Strategy with the US/EU - R & D LT and set the future vision and roadmap of the India Site for Data Science Application in Drug Development - Clinical Trials, Computational Biology - Human Genetics Research, AI/ML based Clinical Biomarker Data Analyses for Phase I, II and III trials of clinical assets across multiple therapeutic areas.
• Led multiple early and late stage drug discovery/development projects in collaboration with various Global Research and Development Teams to support Phase I, II and III biomarker objectives of clinical assets by using multi-omics datasets on patients suffering from various diseases (Received ‘R & D Impact Award’).

Sep 2018 – Aug 2020 · 1 yr 11 mos

• Built and led a new team of around 20 Data Scientists and Data Engineers.
• Managed cross-functional stakeholders by understanding their individual unmet needs and converting them to tangible problem statements based on measurable Business Metrics.
• Worked as the Technical Lead to build a new app, based on novel patient-level and study level Efficacy-Safety metrics using Unsupervised Machine Learning (PCA, PLS, t-SNE) and Statistical inference to discover hidden patient subpopulations showing varying levels of clinical efficacy for a candidate drug. This App can lead to the discovery of hidden patient subpopulations - detecting novel biomarkers, which can be used to select patients for a Phase 3 Clinical Trial, ensuring highest drug efficacy and safety.
• Worked as the Technical Lead on an RWE generation project aimed towards finding novel Marketing Insights for a Novartis drug by using multiple Statistics and Machine Learning based methods: Cox Semi-parametric Regression, K-Means Clustering and Random Forest on the temporal Diagnostic Test Results & Comorbidity Profiles extracted by Hive-SQL Queries from a large Electronic Health Record Database.
• Worked as the Product Manager and Technical Lead to build the First in Industry “Culture Sense” App in R-Shiny (deployed in AWS) that graphically illustrates the Country & Business Unit level Culture Patterns of the company using Unsupervised Learning on multiple Culture Survey Datasets and uses Supervised Learning to measure the observed effect of a change in different culture parameters on the 4 key company performance metrics: Net Sales, Attrition Rate, Operating Income and Market Share.
• Redesigned the Global Performance Management System of the company by analyzing multiple datasets from two different Reward Models, using a Novel A/B Testing Framework (Won ‘Star Award’).

Sep 2017 – Aug 2018 · 11 mos

• Worked as the Lead Statistician on diverse problems based on Health Economics data, developing methods to build Cost-Effectiveness models for upcoming drugs and conduct Network Meta Analyses (NMA) on extracted data points from published research works on Clinical Trials for various head-to-head drug comparisons, in both Frequentist as well as Bayesian paradigms.

Jun 2015 – Aug 2017 · 2 yrs 2 mos · Kalyani, India

• Worked as the Principal Investigator for various Data Science Projects in Genomics with potential applications in Healthcare.
• Was awarded a Department of Biotechnology (DBT) (Govt. of India) sponsored Research Grant for the development of Softwares, implementing various statistical and machine learning based analysis & visualisation tools for Big Data.
• Developed novel Statistical Modelling and Machine Learning methods and softwares for various complex problem statements based on high-dimensional Datasets (Big Data) generated from:
• Illumina Hi-Seq - Differential Gene Expression Analyses
• Illumina 450k Array - Differential Methylation Analyses
• CHIP-Seq - Biomarker Identification from H3K4 and H3K27 Histone Modifications in E7 Oncoprotein Infected and Non-Infected Cervical Cancer Patients.
• Ion-Torrent Targeted Resequencing - Novel SNP Identification in Pancreatic Cancer Patients.

Jun 2013 – May 2015 · 1 yr 11 mos · Boston, Massachusetts, United States

• Worked under the supervision of Prof. Rafael Irizarry, on developing Innovative Statistical Modelling and ML based Methods and Softwares for various challenging problems in Epigenomics and Transcriptomics, with potential applications in discovering Novel Drug Targets for multiple Disease Areas.
• Project:
• Capturing the Genome-wide Correlation Pattern of Gene Expression and DNA Methylation for various human tissues and finding the False Discovery Rate (FDR) controlled Differentially Methylated Regions (DMRs) between any two tissue types (or biological conditions) by using Whole Genome Bisulfite Sequencing (WGBS) data.
• Publication:
• https://academic.oup.com/biostatistics/advancearticle/doi/10.1093/biostatistics/kxy007/4899074
• Software:
• https://bioconductor.org/packages/release/bioc/html/dmrseq.html

Jun 2013 – May 2015 · 1 yr 11 mos · Boston, Massachusetts, United States

• Developed novel Statistics and Machine Learning methods and softwares based on data from Epigenomics - DNA Methylation (Whole Genome Bisulfite Sequencing: WGBS, Reduced Representation Bisulfite Sequencing: RRBS)
• Transcriptomics (Microarray, RNA-Seq, Human Transcriptome Array: HTA)

Aug 2011 – Apr 2013 · 1 yr 8 mos

• Worked on innovating novel Statistical Modeling and Machine Learning methods to solve various challenging problems based on Big Data in the fields of Genomics, Survival Analysis and Bioengineering
• Dissertation Title:
• Novel Methods Based on Regression Techniques to analyze Multistate Models and High-dimensional Omics Data.

Aug 2009 – Jul 2011 · 1 yr 11 mos

• Courses: Statistical Computing with R, Advanced Statistical Inference, Advanced Survival Analysis, Bayesian Inference