Aug 2023 – Present · 2 yrs 10 mos

• Helping Red Hat build Generative AI capabilities to bring operational efficiencies
• Built an production grade AI Assistant using RAG approach that helps 10k sales reps being more efficient, deflecting more than 30% support tickets generated since launch. This application is hosted on OpenShift AI using MLOps principles.
• Built natural language to SQL AI assistant using RAG approach for sales and operations team to help generate quick analytics and answer financial questions
• Built training foundation builder AI that can help generate baseline training content using RAG approach, that helps the training and enablement team get started
• Building an AI assistant that can help enterprise users identify security vulnerabilities in their products using CVE and SBOM information.
• Managing and mentoring the interns on the team

Jan 2021 – Jul 2023 · 2 yrs 6 mos

• Helping Red Hat augment planning and forecasting process with standardize statistical predictions by creating financial time series forecasting model to help business teams and sales teams make better decisions.
• Helping Red Hat make strategic, data driven decisions by providing actionable insights, by leading the team on creating end to end Quota recommender model that helps plan and compensate sales reps
• Managing a team of data scientists, data analysts and data engineers,focused on delivering end to end analytics solutions to leadership
• Helping to create strategy and vision for the team, by guiding them in their projects and working with the senior management of the company
• Working with internal stakeholders to gather requirements and drive adoption of the analytics solutions that the team is building
• Helped build the team by hiring right talent and along with coaching and mentoring the team

Mar 2020 – Jan 2021 · 10 mos

• Technical lead for group of projects within 40 member analytics team
• Created bookings and revenue time series forecast models for Red Hat with average 97% accuracy over time at global level. This model uses grouped time series (GTS) method to forecast at 1000s of levels within business
• Created a monte-carlo simulation model that would help explain the time series forecast to end users for easy interpret-ability

Aug 2018 – Mar 2020 · 1 yr 7 mos

• Worked on creating a classification based machine learning model for predicting likelihood of a deal closing based on multiple parameters with 80% accuracy over time.
• Helped with creating a classification model on text data using NLP techniques, which eventually fed into another machine learning model
• Was part of the team that created multiple new data sets around revenue and bookings information using python scripts by connecting to sales and other Red Hat systems
• Worked on a propensity model which had 4 different models underneath - Likelihood of customer renewing (or churn) - likelihood of customer expanding (or contract) - likelihood of customer adopt a new product (addition) - likelihood of customer return (or net new).

Nov 2015 – Jul 2018 · 2 yrs 8 mos

• Created a snapshot data with millions of records using python which helped start the forecasting models and automated it using Airflow
• Worked on time series renewal forecasting model to using prophet package for Red Hat through its subscription based model
• Worked on creating machine learning model using xgboost to predict likelihood of a customer paying upfront