Feb 2022 – Present · 4 yrs 4 mos

• Conducting the mentored learning sessions for the cohorts of Data Science and Machine Learning certification program from MiT Institute for Data, Systems, and Society (IDSS) in collaboration with great learning.
• Link to the program: https://idss.mit.edu/about-us/
• I also started to mentor the post graduate certification program on AI and ML for business applications with UT Austin.
• Link: https://www.mygreatlearning.com/pg-program-online-artificial-intelligence-machine-learning

May 2021 – Apr 2022 · 11 mos · United States

• Data Scientist at Security Business Unit of VMware Carbon Black.
• Built prevalence-based scoring model that effectively suppresses the noise (false positives alerts) by x% , saving the time and resources for threat analysts and hunters
• Worked on building an unsupervised machine learning model to cluster the command lines, identifying anomalous command lines that can result in malicious behavior (algorithms explored in this project are- Doc2Vec, UMAP, K-Means, and HDBSCAN)
• Created data transformation pipeline in Apache Airflow to orchestrate and schedule the jobs for batch preprocessing, which leads to effective feature engineering for training ML models
• Designed architecture and threat models, in collaboration with engineering team to productize ML model
• Worked with ML engineers to understand, establish and follow CI/CD pipeline for MLOps

Dec 2019 – May 2021 · 1 yr 5 mos

• Worked as a Data Scientist for Azure Core Security Service team under Microsoft Cloud + AI.
• Worked on detection models to find patterns, detect advanced threats, bad actor techniques, anomalous or suspicious activity, and active risks to Azure network and systems
• Conducted end-to-end data analytics and reporting on UDP based DDoS attacks using unsupervised learning methods like clustering and pattern analysis to detect and fix the vulnerabilities like OS baseline configuration, open ports/protocols in network security groups
• Performed exploratory data analysis on internet exposed endpoints, curate the raw data from different sources for machine attribution, and generate reports providing actionable insights
• Created a baseline model of Incidents data to help calculate and optimize mean TTA/ TTM (Time To Acknowledge, and Time To Mitigate) metrics for security incidents, meeting the SLAs on those metrics, and closing gaps based on root causes like ticket routing/transfer issues
• Expanded the knowledge of data science within the team by mentoring data analysts to help them learn/grow in the data science domain, and collaborated with data engineers/developers to create, manage and monitor the data pipeline for the ETL workflow

Jan 2019 – Jan 2020 · 1 yr

• Worked on building machine learning model for Credit Card debt collections to predict whether customers will contact and pay once their account is delinquent. This model scores the conditional probability based on historical contacts data of each customer across different communication channels like phone call, email, mobile or desktop app, branch visits, and text data. Completed data ingestion phase- working along with data SMEs from various departments to collect and ingest the training data, sampling the data and defining the base population. Worked with data engineers in the team to build data flow diagram, entity relation diagram; and use those artifacts to streamline the data pipeline and complete data transformation/validation.
• Tools & Technologies: Python, Git, SQL, Jupyter notebook, Pyspark, H20 ML, SkLearn, SAS

Sep 2014 – Mar 2019 · 4 yrs 6 mos

• Collecting software scan data from network and process it to identify and remove vulnerable & non-compliant applications using data analytics and modeling.
• Network traffic data analysis (approx. 10K endpoints streaming gigabytes of data everyday in Research Development & Test Environment): cleaning/sampling the data, creating predictive models to categorize, block and remediate the targeted attacks and threat vectors.
• Work with security operation teams like forensics and incident management to collect required data, and perform analysis to aid their investigations.
• Understand/ follow policies regarding information/cyber security like NIST, FIPS, RMF, FedRamp and others.
• Tools & Technologies: Python, PostgreSQL, JIRA collaboration tool, Splunk log analytics, Vulnerability Management System (VMS), Enterprise Mission Assurance Support Service (eMASS), Host Based Security System (HBSS), Assured Compliance Assessment System (ACAS)

Feb 2010 – Aug 2014 · 4 yrs 6 mos · Fort Irwin, California

• United States Army Service member.