Pranit Vyas Email & Phone Number

Vienna, Austria

− Developed a personalised training plan recommender, boosting user engagement with a 72% top 3 accuracy− Enhanced the Virtual Race experience, increasing participation by 7% and race completion rates by 14%, contributing directly to user retention.− Helped business in identifying Running User Cohorts to better understand runners and cater to their needs.−.

Hyderabad, Telangana, India

- Implemented single shot detector algorithm to identify check entities and speed up backend process.- Developed a Shiny app for Call Volume Forecasting, reducing forecast error from 15% to under 10%, streamlining customer service operations and enhancing resource allocation.- Developed loyalty score using feature engineering and k-means clustering for.

Hyderabad, Telangana, India

- Retrained spacy ner model for custom entities (Fund Names, Marketing Material, Follow Up, Tools and Modes) from sales call transcriptions with 85% recall and 89% precision- Developed sales as well as growth leads model with 70% recall and 50% precision. - Identified patterns to justify 30% of the misclassifications in leads generation model- Created.

Mumbai Area, India

- Created a logistic regression model achieving 89% accuracy, optimising email campaigns and driving better physician engagement across five promotional channels.- Developed pattern recognition algorithm to identify devices belonging to HCPs by studying devices data in the targeted locations collected through banner ads.- Created algorithms to assign.

Mumbai Area, India

- Measured the impact of digital promotions for over 30 brands using propensity score matching and analysis of covariance - Created R-Shiny apps for the existing processes to increase efficiency and incorporate evolving digital promotion products - Developed forecasting models to predict future sales - Collaborated with US project management team to find.

Pune Area, India

Worked on projects related to Churn Prediction and Pathway analysis.Responsible for data pre-processing, data cleaning, exploratory data analysis and visualization, transformation and deriving new variables for feature selection and machine learning algorithms (supervised and unsupervised).