Feb 2022 – Apr 2022 · 2 mos

Apr 2021 – Feb 2022 · 10 mos

• 1 ) I am technical lead (AI) for Spin's Next generation scooters which will be using advanced Computer Vision; our NextGen scooters will optimize scooter's reliability and ensure rider's safety.
• 2) Developed models for identifying fraud on large scales. The goal of the Fraud modeling team is to
• Define fraud-associated metrics
• Identifying common traits do fraudulent trips.
• Telltale signs for stolen credit cards
• 3) Built a bayesian data driven model to estimate the reliability of the electric scooters.
• 4) Deployment optimization for the electric scooter and bikes
• 5) Developed and deployed Helmet detection model using GCP AutoML vision.

Jan 2020 – Apr 2021 · 1 yr 3 mos · San Francisco

• As a data scientist in the Customer Engagement Engine (CEE) team of Wells-Fargo. I help determine technology partner, design, build and operate the CEE platform for the enterprise, a core component. The team’s objective is to build machine learning models that help engage customers in a personalized one-to-one dialogue, creating timely and relevant communications to serve customer’s needs.
• Develop strategic analyses to maximize profits and operational efficiency in the domain of credit card and bank products/services marketing.
• Developed machine learning personalization models (customer lifetime value, product and service purchase) to enhance customer engagement and optimize long-term profitability.
• Conduct strategic driver analyses of key customer transactional behavior to provide tactical business recommendations using formal statistical approaches.
• Leverage regularization and ensemble methods to estimate the probability of customer behavior. The objective is to develop more effective customer segmentation to promote marketing activities and detect future behavior.
• Evaluate classification performance of various machine learning algorithms (e.g. random forest, gradient boosting, regularization, logistic regression, K-nearest neighbor, discriminant analysis, naïve Bayes) to identify the best predictive outcome and optimal business performance.

Oct 2016 – Jan 2020 · 3 yrs 3 mos · Sunnyvale, CA.

• 𝙏𝙝𝙚 𝙜𝙡𝙤𝙗𝙖𝙡 𝙡𝙚𝙖𝙙𝙚𝙧 𝙞𝙣 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙖𝙡 𝙃𝙚𝙖𝙡𝙩𝙝 𝙈𝙤𝙣𝙞𝙩𝙤𝙧𝙞𝙣𝙜 𝙖𝙣𝙙 𝙈𝙖𝙣𝙖𝙜𝙚𝙢𝙚𝙣𝙩 (𝙎𝙃𝙈) 𝙩𝙝𝙖𝙩 𝙙𝙚𝙨𝙞𝙜𝙣, 𝙗𝙪𝙞𝙡𝙙, 𝙖𝙣𝙙 𝙢𝙤𝙣𝙞𝙩𝙤𝙧 𝙩𝙝𝙚 𝙝𝙚𝙖𝙡𝙩𝙝 𝙖𝙣𝙙 𝙘𝙤𝙣𝙙𝙞𝙩𝙞𝙤𝙣 𝙤𝙛 𝙙𝙞𝙫𝙚𝙧𝙨𝙚 𝙨𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙨 𝙧𝙖𝙣𝙜𝙞𝙣𝙜 𝙛𝙧𝙤𝙢 𝙖𝙞𝙧𝙘𝙧𝙖𝙛𝙩, 𝙖𝙪𝙩𝙤𝙢𝙤𝙗𝙞𝙡𝙚𝙨, 𝙝𝙚𝙖𝙫𝙮 𝙢𝙖𝙘𝙝𝙞𝙣𝙚𝙧𝙮
• Provide guidance on the design and implementation of an integrated end-to-end process based on proven industry best practice guidelines for SHM applications, big data, and analytics. Formulate organization's project management methodology from ground up, affecting the visibility of and communication on engagements; results include improve on-time deliverables, budget controls, and client satisfaction. Leveraged expertise in developing the technical discipline to create process improvements through data-driven decision making
• ➢ Strategically Pioneered and executed the Point Process Filter based prognostics module for structural health monitoring software for CRRC China - involved signal processing and analyzing ultrasonic waves/sensors using statistical learning methods to monitor operational life of commercial high-speed trains
• ➢ Supervised, and spearheaded the installation of load monitoring software – sensors and software in KFX – fighter planes being built by Korean Aerospace Industry, a South Korean aerospace and defense company. Structural Health Monitoring (SHM) and Load Monitoring software placements will happen first time on military aircraft for the production process in worldwide
• ➢ Refactored Legacy codes of Acellent Software to increase efficiency and accuracy of the software. Fixed reported bugs by converting old MATLAB based into more readable C++ codes
• ➢ Accomplished and authored grant proposals for Small Business Technology Transfer Research (STTR) from a variety of sources such as Department of Defense (DoD), NASA, NSF and Department of Energy (DoE) for Acellent Technologies Inc

May 2016 – Sep 2016 · 4 mos · Washington D.C. Metro Area

• Research Project: https://www.iarpa.gov/index.php/research-programs/scite
• 𝙏𝙝𝙚 𝙎𝙘𝙞𝙚𝙣𝙩𝙞𝙛𝙞𝙘 𝙖𝙙𝙫𝙖𝙣𝙘𝙚𝙨 𝙩𝙤 𝘾𝙤𝙣𝙩𝙞𝙣𝙪𝙤𝙪𝙨 𝙄𝙣𝙨𝙞𝙙𝙚𝙧 𝙏𝙝𝙧𝙚𝙖𝙩 𝙀𝙫𝙖𝙡𝙪𝙖𝙩𝙞𝙤𝙣 (𝙎𝘾𝙄𝙏𝙀) 𝙥𝙧𝙤𝙜𝙧𝙖𝙢 𝙛𝙤𝙘𝙪𝙨𝙚𝙙 𝙤𝙣 𝙢𝙤𝙙𝙚𝙡𝙞𝙣𝙜, 𝙛𝙤𝙧𝙚𝙘𝙖𝙨𝙩𝙞𝙣𝙜 𝙖𝙣𝙙 𝙞𝙙𝙚𝙣𝙩𝙞𝙛𝙮𝙞𝙣𝙜 𝙩𝙝𝙚 𝙥𝙤𝙩𝙚𝙣𝙩𝙞𝙖𝙡 𝙞𝙣𝙨𝙞𝙙𝙚𝙧 𝙩𝙝𝙧𝙚𝙖𝙩𝙨 (𝙚.𝙜., 𝙪𝙨𝙚𝙧𝙨 𝙬𝙝𝙤 𝙖𝙧𝙚 𝙨𝙪𝙗𝙨𝙩𝙖𝙣𝙩𝙞𝙖𝙡𝙡𝙮 𝙙𝙞𝙨𝙨𝙖𝙩𝙞𝙨𝙛𝙞𝙚𝙙 𝙬𝙞𝙩𝙝 𝙬𝙤𝙧𝙠, 𝙨𝙩𝙧𝙤𝙣𝙜𝙡𝙮 𝙤𝙗𝙟𝙚𝙘𝙩 𝙩𝙤 𝙨𝙥𝙚𝙘𝙞𝙛𝙞𝙘 𝙥𝙤𝙡𝙞𝙘𝙞𝙚𝙨, 𝙖𝙘𝙩𝙞𝙫𝙚𝙡𝙮 𝙚𝙣𝙜𝙖𝙜𝙚𝙙 𝙞𝙣 𝙚𝙨𝙥𝙞𝙤𝙣𝙖𝙜𝙚 𝙖𝙘𝙩𝙞𝙫𝙞𝙩𝙞𝙚𝙨, 𝙥𝙧𝙤𝙖𝙘𝙩𝙞𝙫𝙚𝙡𝙮 𝙝𝙞𝙙𝙞𝙣𝙜 𝙪𝙣𝙙𝙚𝙘𝙡𝙖𝙧𝙚𝙙 𝙨𝙤𝙪𝙧𝙘𝙚𝙨 𝙤𝙛 𝙞𝙣𝙘𝙤𝙢𝙚, 𝙚𝙩𝙘.).
• ➢ Developed Copula based algorithm for Scientific Advances to Continuous Insider Threat Evaluation (SCITE) program which facilitated in winning Grant solicitation from The Intelligence Advanced Research Projects Activity (IARPA).

Jan 2012 – May 2016 · 4 yrs 4 mos · Tallahassee, Florida, United States

• ➢ Develop new data driven algorithms for better data handling, damage detection, prognostics and damage classication using Lamb wave sensors.
• ➢ Develop innovative approaches that change the behavior of coastal businesses and/or residents so that they are more resilient when making decisions and taking actions in a risk-prone environment.

May 2011 – Dec 2011 · 7 mos · Nepal

• Scheduling classes, administrative work

Jan 2010 – May 2011 · 1 yr 4 mos · Birgunj, Nepal

• Scheduling Production lines
• Keeping track of daily production and reporting it to higher management.