Rajat

• Leading and mentoring a team of MLEs and Data Scientists to complete various Data Science Projects
• Leveraging machine learning for solving retail problems like – Customer Segmentation, Forecasting Demand, Pricing/ Promotion Analytics
• Introducing the best practices for implementing Data Science techniques, Version Control, Data Science engineering, automation, etc.
• Communicating with the business and leadership to understand the exact business problems to be solved using data
• Introducing MLOps best practices for model deployment, model monitoring, alerts, etc,.,
• Automating the infrastructure through AWS cloud formation / Terraform and serverless deployments.

Select Environment & Tools Used: Python, Spark, Scala, Azkaban/ Airflow, SQL, Tableau, AWS (Sagemaker, EMR, EC2, EKS), Docker, Kubernetes

• Created Large Volume Data Pipelines using Spark for several use cases. Used mix of tools like: Spark, Scala, Azkaban, etc. to finish and automate the flows. Wrote ETL pipelines for signal processing.
• Used Airflow / Azkaban to orchestrate the data flows to submit the spark jobs to EMR / Glue.
• Optimized spark jobs to reduce the resource utilization and improve efficiency
• Developed and productionized a CTR prediction model using Python to forecast the CTR’s of different properties on the web and app.
• Developed and deployed the gender prediction model to improve the known gender by 25%
• Performed feature engineering and made the features available to various teams for campaign optimization purposes.
• Tableau Visualizations: Created dynamic dashboards to depict actionable insights from Analytics

Select Environment & Tools Used: Spark, Scala, Python, Azkaban/ Airflow, SQL, Data Studio, CI/CD (Jenkins), Docker

• Customer and Site Segmentation: Implemented various end to end solutions for clustering problems
• Applied Machine Learning algorithms to predict the likelihood of customers responding to a campaign and to launch nationwide customer loyalty campaigns
• Churn / Upsell Models: Built the predictive models to reduce churn and upsell premium products
• Pricing and Promotion Analytics: Used time series techniques to find out the impact of price changes for different set of customers
• Text Analytics: Sentiment Analysis and Topic Modelling to identify the drivers of various products using the customer reviews
• Implemented Market Basket Analysis, Network Analysis, SKU Assortment for a retail client
• Trained & compared results using various linear, boosting and stacking models: XGBoost, Random Forest and Linear algorithms with tuned hyperparameters for each SKU
• Trained Technical Sales team on Technical & Soft Skills: AI for Business, Problem Solving, Communication Skills, Collaborative Problem Solving, Creative Thinking, Prototyping
• Helped set up ETL pipelines and Data Lake for an automobile client
• Applied knowledge of ML and AI techniques for Sales Datasets
• Used topic modelling (NLP) to automatically classify reviews into various topics and then used sentiment analytics on the top of it to ascertain public’s opinion for that sku and topic.
• Automated several manual workflows saving considerable time for supply chain and marketing analysts.
• Regularly presented Data Science capabilities, research, and analysis at the CEO level.

Environment & Tools: SQL / Database, R, Python, Tableau, Advanced Excel, PowerPoint, Java, Unix