• Subject matter expert for Supportability of Anaplan's Metadata Platform, responsible for testing new features and reviewing upcoming releases, ensuring the customer experience is not impacted negatively. Supported the transition to a geographically distributed metadata platform (mySQL to Amazon Aurora)

• Provided support to multiple large clients (Google, Amazon, Louis Vuitton) with case management through Salesforce, working closely with Engineers, customers, Business Partners, and Account Executives. 150+ cases resolved and 100+ JIRAs filed.

• Participated in incident management to analyze customer impact and perform internal testing.

• Implemented JIRA-Slack integration alerting for several projects, to automate JIRA alerting.

• Mentored interns as well as new hires.

• Triage and handle cases passed on from L1/L2 teams to provide high-level support leading to reporting bugs or platform enhancement requests.

• Using Splunk to perform detailed log investigations, create service monitoring dashboards, and set up alerting.

• Represented the Support team to prospective/existing customers and partners at the Anaplan Live conference in June 2022.

• Assuring the quality of evolving Self-Driving Car (SDC) software versions by overseeing simulation test results, filing bugs, and keeping track of the bugs to ensure the operational safety of the SDC fleet on the road using in-house tools and workflows.

• 600+ bugs filed which translated to significant software improvement.

• Creating and maintaining documentation, designing test cases, and validating test results of the newer software versions. Working closely with the engineering team and testing teams to ensure the quality of the testing process.

• Analyzing productivity metrics of the team using Google Sheets and implementing actionable changes based on in-depth statistical analyses to boost team-wide productivity.

• Training junior team members to teach efficient use of tools to perform different workflows.

Worked on an array of projects ranging from data mining, data wrangling, visualizations and EDA, inferential statistics and various machine learning models. Learnt implementation of various python libraries - pandas, numpy, seaborn, matplotlip, sklearn, statsmodel.

• Used Qunandl API to analyze stock ticker for companies on the Frankfurt Stock Exchange (FSE) including highest and lowest opening bids, intra day changes, and trading volumes

• Set up an end to end hypothesis test to validate notions surrounding human body temperature. Employed various techniques such as hypothesis testing, confidence intervals, bootstrapping - SciPy, Pandas, Matplotlib, and other Scientific computing packages in Python.

• Performed exploratory data analysis to analyze how excess readmission ratio in hospitals correlate to different feature. Delved into rigorous statistics to set up hypothesis tests.

• Implemented linear regression models to predict prices in Boston housing dataset. Compared different models based on F-test, t-test, rsquared values. Built residual plots and qq plots to understand the distribution of data.

• Tuned parameters for logistic regression using mix of cross validation and grid search to classify males and females based on provided height and weight data.

• Performed text analysis using NLP and count vectorization techniques on Rotten Tomatoes movie reviews. Using naive bayes to classify the reviews.

• Implemented K-Means clustering to analyze customer segmentation. Used elbow method, silhouette method and gap statistic to find the best K values. Compared different clustering models.