Associate Consultant with 7+ years of experience and interest in analyzing current developments in data science and machine learning. Holds a master’s degree in business analytics from the University of Minnesota.
Industry and Project Experience
Project Lead, Data Sciences (Oct 2020- Present)
⦿ Refined the client’s promotional practices by building Feature Engineering pipelines in PySpark for Omnichannel ML models to predict HCP prescribing behavior.
⦿ Led the development of a brand/data agnostic A/B Testing pipeline to assess effectiveness of marketing campaigns on new prescriptions and patient enrollment in drug assistance programs; automated one-one and one-many matching using nearest neighbor, propensity scores, and error variance algorithms in Python.
⦿ Improved ingestion of key HEOR metrics by developing an automated Analytics on Demand system in SQL, Python and Linux, and charting patient journeys in APLD claims data.
⦿ Formulated and refined business rules for Regimens, Lines of Therapy, and Source of Business assignments in Oncology.
⦿ Supported client’s Specialty Data Management team by executing anomaly detection workflows using Python in Dataiku; worked on APLD claims data and Customer Master (Physician data) to facilitate data refreshes from third party data vendors.
⦿ Integrated multiple data sources (IQVIA claims, Customer Master) for complete physician information for commercial targeting.
Financial Firm, Minneapolis, MN
Data/Technology Analyst (Jul 2019 – Aug 2020)
⦿ Reduced outage by 25% by predicting P2 (Priority 2) incidents likely to become P1 with 86% accuracy using Neural Network.
⦿ Executed ANOVA and Fisher’s tests to highlight and closely monitor business functions with consistently failed processes.
⦿ Standardized Service Level Objective for ~300 Technology Catalogs by exploring historical requests.
⦿ Reduced forecasted backlog by 8% for the ServiceNow team by executing anomaly detection in Tableau to identify aging requests.
Analytics Lab, Minneapolis, MN
Data Science Consultant (July 2018 – May 2019)
Client: Leading Non-profit in Education Industry in Midwest (Jan 2019- May 2019)
⦿ Influenced efficient resource allocation by designing an A/B testing pilot to assess the performance of 1600 high-ranking students in a low-touch coaching program.
⦿ Identified students in need of special assistance with college applications using predictive modeling (SVM; 91% accuracy).
Client: Hospitality and Entertainment Business (Nov 2018 – Dec 2018)
⦿ Increased annual revenue by $300k+ by predicting bi-weekly demand of hotel occupancy (Random Forest).
⦿ Addressed declining occupancy rates by identifying customer segments for targeted advertisement and hotel discounts.
Client: Mall of America (Jul – Aug 2018)
⦿ Increased potential for cross-selling at gift stores by ~$5 per product through inventory optimization (Market Basket Analysis)
Non-Profit Organization, Mumbai, India
Data Analyst and Technical Writer (Aug 2017 – Mar 2018)
⦿ Informed people about commonly perpetrated sexual crimes in communal places using Association Rules Mining.
⦿ Identified steps to curb sexual harassment by analyzing incidents at railway stations using text analytics and data visualization.
⦿ Explored Twitter data for the #Metoo campaign to identify patterns of gender-based violence using text analytics.
Management Consulting Firm, Bangalore, India
Decision Scientist (Oct 2015 – May 2017)
⦿ Drove decisions for Directors of different teams from exploratory and predictive analyses on medical claims (Anonymous Patient Level Data) using Teradata SQL and SAS.
⦿ Increased visibility of the client’s HIV drug among healthcare providers by executing analysis for an abstract publication and poster, presented at the Academy of Managed Care Pharmacy in 2017.
⦿ Supplemented information for future clinical trials of the client’s skin cancer drug by analyzing its adherence and switching rates in different Lines of Therapy.
⦿ Influenced payers to give preference to the client’s anticoagulant drug over competitors in their drug formularies by comparing their bleeding event rates (A/B testing).
⦿ Improved insurance support for the client’s Renal Cell Cancer drug by executing cluster analysis on Electronic Medical Records.
Projects
⦿ Deep Scalable Recommender System: Developed 2 recommender systems for movie recommendations using collaborative filtering in AWS Sagemaker and DSSTNE, depending on varying problem complexity and data scalability.
Technical Skills
Techniques: Feature Engineering, Machine Learning, Exploratory Data Analysis, Causal Inference (A/B Testing), Statistics, Anomaly Detection, Data Engineering, Business Intelligence, Data Visualization
Education
University Of Minnesota, Minneapolis, MN – Carlson School of Management
Bachelor of Technology – Computer Science and Engineering (Apr 2015)
National Institute of Technology, Bhopal, India
