DATA SCIENTIST XYZ

• Formulating, suggesting, and managing data-driven projects, aimed at furthering the business interests.
• Ensuring proper alignment of analytics with strategic business initiatives encompassing understanding of omnichannel behavior, building recommendation engines, and store viability model.
• Managing full development cycle of planning, analysis, design, and development.
• Working as an ML Engineer on the MLOps platform, whereby designing the end-to-end ML pipeline for client solutions.
• Providing optimized business solutions to top-performing European and US Finance and Healthcare clients.
• Using NLP to perform text sentiment analytics and summarize the results on QlikView.
• Using ML Algorithms like Text analytics, Clustering, Time series analysis, Regression, and Gradient Boosting to build robust models.
• Building robust dashboards for certain models like COE and Market Mix Models.
• Providing advanced analytical solutions to clients from across the globe in multiple domains.
• Automating the Industrialized CI/CD ML Pipeline on the client’s AWS platform.
• Engagement in designing the initial architecture and data model of the Market mix project.

 

My main achievement:

Making the pipeline furthermore optimized by reducing the execution time by 25%. Making the whole ML pipeline industrialized for all future products as well.

 

13/06/2020 – 25/06/2021 Gurgaon, India

DECISION SCIENTIST XYZ

 

 

• Understanding the US Fintech and Healthcare-based Bigdata and through ETL analysis and dashboards, providing/ reporting data stories to the clients to help them take better business decisions.
• Handling end-to-end data analytics pipeline to provide solutions to business problems.
• Using languages like Python, R, and Pyspark to perform the analysis and undergo the forecasting.
• Building robust dashboards for certain models like COE and Market Mix Models.
• Using ML Algorithms like Text analytics, Clustering, Time series analysis, and Regression to build robust models.

 

My main achievement:

Making the dashboard and entire ETL reporting automated so as to increase productivity and available bandwidth for other tasks.

Making the ETL dashboard parameterized thereby making it reusable for future purposes.