• Own aspects of analytics projects from start to finish – pursuing innovative ideas, developing models, uncovering and communicating actionable insights, and implementing business solutions to capture value. • Partner with IT and Engagement Management teams to integrate statistical models within pricing, supply chain, sales and marketing, operation, workflow and other tools towards “automated” and streaming analytics backed solutions. • Engage with internal customers to leverage your critical thinking skills to apply data science modeling solutions. • Work with various functional teams; be involved in end-to-end data-focused solution development and scaling focused on operations challenges.

• Develop data science solutions with agility to deliver meaningful products with the contraint of time and accuracy (minimum rework). This involves model building using machine learning tools such as sas, python, leveraging AWS technologies, data cleaning and manipulation in SQL• Develop high quality code which can be customized with minimum of rework and using source control (such as gitlab)• Provide superior customer experience to our internal customers and partners, by building strong relationships to understand, anticipate and deliver on customer needs• Communicating with the customers and stakeholders, regarding the key insights from the data and how the business can integrate the solution into their current process.• Creating reports and presentations to talk about the insights or highlight on potential business opportunities to business leaders

• Predictive and Preventive analysis of downtime in paper manufacturing plant, processing data from cloud (Amazon AWS S3 and redshift) using Spark and Hadoop Cluster.• Root Cause Analysis of downtime which in turn increases the plant productivity

• Designing the prediction tool for identifying the Forward-Looking Statement based on SEC-Edger’s 8K• Using Classification and Topic Modeling, studied the market trend for similar and dissimilar companies.

• Spearheaded the team of 3 engineers and envisaged the aerodynamic performance improvement by ~21%.• Remodeled the Experimental Designs using DoE & multiple regression, thus eliminating the need for a field test. • Optimized Feature selection and devising parallel computing, I was able to reduce cycle time to 1 day from 1 week.

• Formulated reduced order empirical models for aero-acoustic analysis, of ducts and mufflers. I was able to bring down the cycle time from 4 days to 5 hrs.• Validated the reduced order model with Experimental, Numerical, and Analytical Results with 80% prediction accuracy.