Vyom Bhatt

As a Senior Data Scientist with six years of experience, I possess advanced proficiency in managing end-to-end scalable data science projects on big data platforms. My expertise lies in applied machine learning, deep learning, and NLP using Python and Pyspark. Some of my notable projects include developing customer lifetime value models, market mix models, multi-touch attribution using Markov chains, and recommender engines for e-commerce clients. Additionally, I have experience building data lakes and creating dashboards, among other relevant skills. With a Master's degree in Data Science, I possess strong problem-solving abilities and a passion for utilizing data to drive impactful business decisions.

Implemented scalable data science projects on a big-data platform to drive business performance for Saks.com, a leading luxury fashion e-commerce business.
Trained a Customer Lifetime Value Model using spark, resulting in accurate predictions of the gross margin generated by each customer in the upcoming year.
Developed a Baseline Sales Model by incorporating both internal and macro factors.
Designed and implemented a data lake containing over 600 customer features.
Evaluated the impact of the Saks.com e-commerce business on the Saks stores by identifying store conversions that may be driven due to website interactions.

Handled over 50 clients from different business verticals to provide data-driven digital advertising solutions.
Projects Summary: Market Basket Analysis, API Exploration, Dashboarding, EDA on advertiser data to present actionable insights, Digital advertising campaign optimization.

Leveraged machine learning, statistics, and data analytics to create data-driven solutions for advertisers across different industries.
Developed a YouTube Channel Recommender Engine using NLP-based embeddings.
Designed and implemented a data lake to integrate geo-based data sources.
Trained a Website Conversion Propensity Model using web browsing behavior data.
Designed and implemented a Marketing Mix Model.
Developed and applied Shapley value and Markov chain techniques to model Multi-touch Attribution.

Leveraging machine learning, statistics and data analytics to create data-driven solutions for advertisers across different industries.
Developed a YouTube Channel Recommender Engine using NLP-based embeddings to help advertisers make advertising decisions on the platform.
Designed and implemented a data lake to integrate geo-based data sources and leveraged supervised machine learning to model top geographies for effective ad targeting.
Trained a Website Conversion Propensity Model using web browsing behavior data to predict the likelihood of user purchases.
Designed and implemented a Marketing Mix Model to pinpoint the marketing levers that drive the greatest financial impact for businesses.
Developed and applied Shapley value and Markov chain techniques to model Multi-touch Attribution and accurately measure the impact of channels in multi-channel marketing campaigns.

Handled over 50 clients from different business verticals to provide data-driven digital advertising solutions.
Projects Summary: Market Basket Analysis, API Exploration, Dashboarding, EDA on advertiser data to present actionable insights, Digital advertising campaign optimization.