Rajat is a results-oriented and versatile Data Engineer / Machine Learning Engineer (MLOps) with 5+ years of work experience in writing ETL and data pipelines and deploying ML models. He is talented in solving real-world challenges with business & analytical acumen. He has experience in strategy, business processes & operations across industries. Rajat is proficient with open source tools, and APIs & handling all stages of the data science lifecycle: problem definition, extract data, EDA, data transformation, feature engineering & modeling to improve performance. He has strong hands-on experience in Python, SQL, Tableau, Spark, Docker, AWS & GCP cloud. He has also coached analysts and fellow data scientists in Data Analytics, Big Data, engineering best practices, and quantitative modeling techniquesHire 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.
Select Environment & Tools Used: Python, Spark, Scala, Azkaban/ Airflow, SQL, Tableau, AWS (Sagemaker, EMR, EC2, EKS), Docker, Kubernetes, Terraform, CloudFormation, Serverless, MLOps
Created Large Volume Data Pipelines using Spark for several use cases. Used a mix of tools like Spark, Scala, Azkaban, etc. to finish and automate the flows. Wrote ETL pipelines for signal processing. Performed feature engineering and made the features available to various teams for campaign optimization purposes. Developed and deployed the gender prediction model to improve the known gender by 25%.
Environment & Tools Used: Spark, Scala, Python, Azkaban/Airflow, SQL, Data Studio, CI/CD (Jenkins), Docker, EMR, Glue, Tableau, GCP, S3, Redshift, Big Query.
He worked with customer and site segmentation implementing various end-to-end solutions for clustering problems. He applied Machine Learning algorithms to predict the likelihood of customers responding to a campaign and to launch nationwide customer loyalty campaigns. Built predictive models to reduce churn and upsell premium products. He regularly presented Data Science capabilities, research, and analysis at the CEO level.
Environment & Tools: SQL / Database, R, Python, Tableau, Advanced Excel, PowerPoint, Java, Unix, NLP, XGBoost, Random Forest, Prototyping, Flask, AI, MySQL, Data Lakes, PowerBI, Redshift, S3, EC2, PostgreSQL