Tanumoy Ghosh Email & Phone Number

Pittsburgh, Pa, Us

Gen AI, Content Recommendations, Search and Information Retrieval

Beaverton, Or, Us

Leading a full-stack ML team focusing on E2E development and MLOps lifecycle for LLMs and CV algos that power consumer experiences. • Search Relevance and Ranking• ML Algorithmic Merchandising• Visual and Conversational Search• Nike By You Recommender• Innovation Direct• Transaction Fraud• SNKRS

San Francisco, California, Us

Leading the Data Science team that drives the ML algorithms to measure the adoption and growth of Salesforce's products among its customers.

San Francisco, Ca, Us

Leveraging AI-ML algorithms and advanced statistical modeling to drive personalized product and customer experiences Deep Learning Frameworks / LSTMs and CNNs to predict sequential product buys and forecast sales for Rodan & FIelds's product line. Designing and implementation of build - test - deploy pipeline for all of our ML algorithms / statistical models using Azure DatabricksA/B Testing, Multivariate Testing and Measurement, Design of Experiments, Parametric and Non-parametric InferenceCausal Inference and Causal Impact Studies using Bayesian Structural Time Series Models for Digital Marketing Geo StudiesTech Stack: Google Cloud Platform, Azure Databricks, R, Python, SparkML

San Francisco, California, Us

Leading the Customer Data Science Team of 9 data scientists focused on developing the predictive intelligence behind Customer 360 platform through machine learning algorithms and advanced statistical models. Tech Stack: Azure, Domino DataLabs, R, Python, SparkML, Hadoop

San Francisco, California, Us

Leading a team of 7 data-scientists and responsible for conceptualizing, implementing and testing advanced statistical models / machine learning algorithms in the customer data science space. Working towards building the customer data science model development and deployment platform in partnership with Informatics and Governance Team.• Catalog Response - Deep Learning ANNs, Adaptive Boosting, XGBoost, SVM• Discount Sensitivity - Deep Learning, Adaptive Boosting, SVM, GBM• Propensity Models for credit card spend - Logit Models• Category Preferences - AdaRank, LambdaMart, RankNet, Coordinate Ascent through RankLibSoftware: R, Python, Spark, SAS, Hive/Hadoop, OracleLibraries: Tensorflow, H2O, MXNet, Caffe, Torch, LeNet, Darch, MLlib 2.1, Adabag, XGBoost, GBM, Kernlab, KlaR, Caret

San Francisco, California, Us

Leading a team of 5 senior data-scientists / data scientists responsible for driving advanced analytics covering different aspects of customer behaviour.• Product Division and Category Preferences• Response Propensity - Direct Mail and Email Campaigns• Lifetime Revenue Model• Discount Sensitivity• Redemption Behaviour• Attrition Risk• Propensity to Return• Campaign/Promotion EffectivenessModels / Algorithms : • Ranking Algorithms - RankLib• Fixed Effects and Mixed Effects Logistic Regression• Support Vector Machines• Adaptive Boosting• Gradient Boosting and Extreme Gradient Boosting• CART and C5.0 Decision Trees• Pareto NBD Models • Linear Regression, Negative Binomial RegressionSoftware: R, Python, SAS, Hadoop/Hive, Oracle

Driving advanced analytics related to different aspects of customer behaviour primarily in the retail sector.• Response Propensity• Revenue Forecasts• Redemption Behaviour• Attrition Risk• Propensity to Return• Campaign/Promotion EffectivenessModels / Algorithms :• Fixed Effects and Mixed Effects Logistic Regression• Bootstrapping • Support Vector Machines• Adaptive Boosting• Random Forests• CART and C5.0 Decision Trees• Linear Regression

Fenchurch Street, England, Gb

Was responsible for implementing statistical models / machine learning algorithms to understand and predict customer behaviour towards different products like annuities, pensions, bonds etc.• Propensity Modeling• Customer Insights• Campaign Insights• Process AutomationModels / Algorithms : Fixed Effects and Mixed Effects Logistic Regression, Support Vector Machines

Mumbai, Maharashtra, In

Focus was on building Statistical Models around survey and retail transaction data for leading Market Research Agencies and Retailers in the US. I have worked on Survey Quality Control, Multivariate Methods, Predictive Modeling, Customer Segmentation, Discrete Choice/ Conjoint Analysis Studies, Time Series Analysis, Sampling Methods and Optimization Techniques. I developed my expertise in building statistical models using SAS, SPSS, Statistica etc and creating VBA based GUIs.• Data Mining Methods• Survey Quality Control• Multivariate Methods• Predictive Modeling• Customer Segmentation• Discrete Choice/ Conjoint Analysis Studies• Time Series Analysis• Sampling Methods• Optimization Techniques

Postgraduate Degree, Applied Statistics And Statistical Software

Bachelor'S Degree, Majors - Statistics, Minors - Economics And Mathematics