Sushrut S.

I am a data scientist with extensive experience working in bio-pharmaceutical and healthcare industries. I specialize in developing solutions based on artificial intelligence (AI), machine learning (ML) and deep learning (DL) to solve varied business problems. I am a professional data scientist with 8 years of experience in the pharmaceutical, healthcare and life sciences industries. I have participated in 4 hackathons and have been a finalist in every single one of them. I have also won 2 of these 4 hackathons and work with the mindset to bring innovative ways of problem solving. I work with the 3 Is at the core of my work ethic: Investigate Introspect Innovate My specialization includes the following areas of business: Segmentation Forecasting Impact assessment Driver analysis Disease detection Market Mix Modeling Sequencing NLP Within the data science, AI and machine learning domain, I am adept at the following aspects: Data quality and sanity checks Data cleaning, preparation, transformation, evaluation Creation of analytical datasets Exploratory data analysis Feature elimination, engineering, selection Dimensionality reduction Unsupervised machine learning: Clustering Supervised machine learning: Classification, Regression Time series forecasting Wordcloud creation Sentiment analysis Sequencing algorithms Geospatial models I use RStudio as my preferred platform and R as the preferred language for data science projects. I am adept at the following things: 1. Segmentation & Targeting (Physicians, Patients, Hospitals, Pharmacies) 2. Supply Chain Analytics (Lead Time Analysis, Segmentation, Cost-to-Serve, Go-to-Market Strategy) 3. Machine Learning (Classification & Regression) 4. Geo-spatial Models (Haversine Distance, Air Distance, Road Distance) 5. Scoring Models (Weighted Matrix Scoring) 6. R and RStudio 7. Excel 8. VBA I have extensive experience with machine learning algorithms like: 1. Random Forest 2. Gradient Boosted Trees (GBM) 3. Linear Regression 4. Multinomial Logistic Regression 5. Artificial Neural Networks 6. XGB 7. k-means 8. Hierarchical Clustering 9. LCA 10. Cross-Validation (LOOCV, LGOCV, k-fold) 11. Sampling (SMOTE, Over, Under)