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Co-Founder Autosure.ai | GTM-India Robotics | Chief Data Scientist | Data Science Advisor| IITian
As the Chief Data Scientist at Traffk, a leading insurance and distribution platform, and the Co-Founder at Autosure.ai, a startup that is building the connected vehicle infrastructure and ecosystem, I have over ten years of experience in applying data science and machine learning to solve complex business problems and create innovative solutions across various industries. My core competencies include data mining, data visualization, machine learning, operations research, forecasting, optimization, and simulation. I am passionate about leveraging data and AI to improve road safety, reduce accidents, and enable smarter mobility. I have designed and developed various analytical frameworks and models to implement analytics for numerous solutions deployed for clients in banking, financial, insurance, and utilities sectors. I am also responsible for leading the entire client accounts analytics deliverables and ensuring the quality and efficiency of the data science platform as a service.
Indian Institute Of Technology
Bachelor of Technology (B.Tech.), Electrical, Electronics and Communications Engineering
N/A – Present
University of Cincinnati
Master of Science (MS), Data Science
N/A – Present
Ottonomy Inc
GTM India Sales
January 1, 2025 – Present
India · On-site
Vismaya
Head of Data Science
January 1, 2025 – Present
Assessli
Chief Data Science Advisor
October 1, 2023 – August 1, 2024
India · Remote
Data Cult
Head of Data Science
March 1, 2023 – February 1, 2025
India · Remote
Traffk - Insurance & Distribution Platform
Chief Data Scientist
April 1, 2022 – August 1, 2023
Autosure.ai
Co-Founder
December 1, 2019 – Present
Hyderabad, Telangana, India
Oracle
Senior Data Scientist-III
June 1, 2019 – April 1, 2022
Reston
Traffk - Insurance & Distribution Platform
Consultant Lead Data Scientist
April 1, 2018 – June 1, 2022
Udacity
Data Science Mentor - Deep Learning Nanodegree
September 1, 2017 – November 1, 2018
Columbus, Ohio Area
Udacity
Data Science Mentor - Data Analyst Nanodegree
September 1, 2017 – April 1, 2018
Columbus, Ohio Area
IBM
Data Scientist
May 1, 2015 – October 1, 2018
Dublin, Ohio
University of Cincinnati
Student
August 1, 2014 – August 1, 2015
Cincinnati Area
Defence Research and Development Organisation
Statistical Research Internship
April 1, 2013 – August 1, 2013
Hyderabad Area, India
Opto Circuits India Ltd.
Student Research Assistant
May 1, 2012 – September 1, 2012
Bengaluru Area, India
MNR Construction Consultancy
Statistical Business Analyst
April 1, 2010 – May 1, 2012
Hyderabad Area, India
Behavior-based Customer Insight for Banking Solution
July 1, 2015 – Present
The solution includes predictive analytics models and interfaces, along with data preparation capabilities that streamline collection and preparation of data for analysis. By using interactive and role-specific dashboards, users can share predictive insights between teams and organizations, which ultimately helps them develop a deeper understanding of their customers, make better decisions and act with greater speed. Using advanced predictive models, the solution analyzes transactions and customer spending behavior to: •Provide insight into customers and their propensities. •Create targeted offers based on anticipated life and financial events as predicted from spending behavior. •Predict the most appropriate action as determined by the analysis. •Proactively shape customer treatments based on anticipated spending and its financial impact. By using customers’ spending behavior, interactions, and anticipated life and financial events as a basis for predictive insight about those customers and their propensities, the solution helps banks deliver more relevant and personalized offers and actions. Banks that are able to present more targeted offers have seen significant improvement in response rates, which has resulted in increases in average deposit balances. In addition, interactions that are relevant to the customer often help reduce attrition by demonstrating a better understanding of customers’ needs.
Behavior Based Customer Insight for Insurance Solution
June 1, 2015 – Present
This solution combines structured and unstructured data from various internal and external sources to help you improve your ability to know and understand each policyholder. Customers want to feel that their insurer knows and understands them. By using more accurate insights into customer behaviors that feed into each interaction to create smarter retention offers, you can increase the likelihood that the next contact with the policyholder will be successful. IBM Behavior Based Customer Insight for insurance enables Insurers to generate customer behavioral segments and insights based on life events. Such segments can help them predict churn, identify risk, improve customer lifetime value and drive interactions and offers. These industry specific models help accelerate the value to insurers in areas such as: • Behavior based customer segmentation • Customer life change event prediction • Churn propensity insight • Customer lifetime value insight • Customer risk insight • Social media insight • Offer scoring by highest likelihood of response • Predictive renewal score • Product profitability insight
Behavior-Based Customer Insight for Wealth Management Solution
June 1, 2015 – Present
IBM’s Wealth Management Client Insights solution generates advanced customer segmentation based on client’s behavioral profile and predicts life events using historical data analysis allowing financial advisors to excavate deep customer insights and provide personalized advice and tailored portfolio recommendations to their clients. The solution includes utilization of IBM’s key differentiation: design input from industry-leading expertise and exploitation of IBM’s unique partnerships with data providers such as Twitter and The Weather Company. The IBM solution provides an architected, end-to-end solution which is delivered as a productized offering with supported components. Behavior Based Client Insights for Wealth Management uses historical life event modeling to identify and categorize significant life events for any recognized behavioral segment. The solution offers drill-down capability that provides access to stores of actionable customer insights. The solution gives financial advisors a more efficient and personalized way to serve their clients. It helps improve operational efficiency support and enhance the overall client experience.
IBM’s Industry Analytics Solutions
June 1, 2015 – Present
Designed and Implemented the Framework for deployment of the Behavior Based Customer Segmentation Insights for various industry clients scalable with client requirements. IBM Behavior Based Customer Insight for Industry solution gives the information and insight that is need to provide proactive service to client's customers. The IBM Behavior Based Customer Insight for Industry solution works with IBM Predictive Customer Insight. The solution includes reporting and dashboard templates, sample predictive models, and application interfaces for integration with operational systems. It uses Industry data related to transactions, accounts, customer information, and location etc. to divide customers into segments based on their spending and saving habits and predicts the probably of various life events. By anticipating customer needs, the solution enables industries to deliver personalized, timely, and relevant offers.
US Bank Forecasting Commercial Mortgages
May 1, 2015 – Present
Applied dynamic time series regression and vector auto regression models to forecast the quarterly mortgage for 2015 and 2016 based on 6 different macroeconomic factors under 3 macroeconomic scenarios.
Wine quality prediction
April 1, 2015 – Present
Created a data mining approach to predict the quality of a wine based on the physicochemical properties of a given wine. Built various models such as Multinomial regression, Ordinal regression, Cluster analysis and Random Forests and observed the performance of each of these methods to develop a mixed model to achieve a higher prediction rate.
Crime Data Analysis of Chicagoland
December 1, 2014 – Present
The primary objective of crime analysis was to assist the police in reducing and preventing crime and disorder. Different characteristics of crime and disorder are relevant in crime analysis, but the three most important kinds of information that crime analysts use are sociodemographic, spatial, and temporal. Sociodemographic information consists of the personal characteristics of individuals and groups, such as sex, race, income, age, and education. Detected correlations between various parameters such as school truancy and a rise in neighborhood burglaries. Understanding complicated patterns and utilized data and applying data science in an effort to make high-crime neighborhoods safer. Ran various analysis such as Discriminant Analysis, Cluster Analysis etc. on the Public Data of Chicagoland and developed our own association rules to the various data variables and statistically predicted the crime, their nature and the safety of localities of Chicago-land using various models such as Multinomial-Regression analysis and Support vector machines and neural networks and observed the performance of each of these methods and develop an mixed model based on regression and neural network to achieve a higher prediction rate. Utilized the crime data for geographic patterns to predict crime “hotspots” and the nature of crime.
Simulation of Campus Recreation Center
October 1, 2014 – December 1, 2014
The UC Campus Recreation Center is an impressive building, with over 200,000 square feet of recreation facilities. A juice bar and a convenience store are also available to students for immediate refreshing during or after a big workout. The CRC has three pools, over 21,000 pounds of weights, a climbing wall, and a suspended track. With a heavy focus on swimming and other aquatic sports, the facility is a big bonus in Ohio’s nasty winter. This project attempts to understand the working and tries to improve the performance and productivity users get out of the Recreation center.Simulated all the three floors and various sections of the recreation center and analysed the sections where the users usually experienced delays and suggested the improvements which improve the usage performance by almost 12 percent than the existing.
Analysis of Olympic Medal Winners (2000-2012) using Tableau integrated SQL Server
September 1, 2014 – Present
The performance of Athletes in the Olympic Games between the years 2000 and 2012 was analyzed using SQL Server and a visualization of the same was done using Tableau. The different editions of The Games under consideration were: Summer Olympics - 2000, 2004, 2008, 2012 Winter Olympics - 2002, 2006, 2010
Parkinsonian Tremor-A non-traditional approach to research Parkinson’s disease and analyze various causal relations
September 1, 2013 – April 1, 2014
Tremor is one of the cardinal symptoms of Parkinson's disease. Up to now, however, its pathophysiology remains poorly understood. Previously, oscillatory coupling at tremor frequency between the subthalamic nucleus und affected muscles was shown. In these studies, however, causality of coupling could not be demonstrated. Thus, we analyzed the statistical causality between intraoperatively recorded local field potentials in the subthalamic area and affected arm muscles during tremor episodes, using squared partial directed coherence, a recently developed causality measure. The analysis identified differential statistical causality patterns for Parkinson's disease patients of the akinetic-rigid subtype during tremor episodes (n=6) versus patients of the tremor-dominant subtype (n=8): for the akinetic-rigid Parkinson's disease patients significantly more cases of the subthalamic region were found to be statistically causal for electromyographic-tremor activity, a result in accordance with the standard basal ganglia model. In contrast, for the tremor-dominant patients, significantly more instances of electromyographic tremor activity turned out to be causal for activity of the subthalamic region
Design of Static-Dynamic piezo resistive pressure transducer
April 1, 2013 – August 1, 2013
There is a need to measure static and dynamic conditions in many gas turbine applications, in particular for combustion instabilities, such as those in the afterburner. The DC and low frequency components are typically used for conventional engine control, while the high frequency data is essential for acoustic screech and rumble diagnostics and control. This project presents a static-dynamic piezoresistive pressure transducer that measures low amplitude, dynamic pressure perturbations superimposed on top of a high pressure through the implementation of low pass mechanical structures. The transducer, which is capable of operating at ultra-high temperatures and in harsh environments, consists of a static piezoresistive pressure transducer, which measures the large pressures on the order of 200psi and greater, and an ultrasensitive, dynamic piezoresistive pressure transducer which captures small, high frequency pressure oscillations on the order of a few psi. The heightened sensitivity in high pressure environments is achieved by filtering the measured pressure of high frequency content through an innovative low pass mechanical filter structure. The large static pressures passed by the low-pass mechanical filter structures are routed to the backside of the dynamic pressure sensor, which results in both the front and the back of the dynamic sensor being exposed to the large pressures within the environment. Therefore, the large static pressures cancel out, and the dynamic sensor only senses the low magnitude, high frequency pressure perturbations. The transfer function characterization of a fully operational static-dynamic pressure transducer over a wide bandwidth. Based upon the analytical and experimental results, the static-dynamic pressure transducer will make it possible for turbine users and manufacturers to implement ultra-sensitive pressure monitoring to reduce compressor and combustion instabilities
Anwesha'13
October 1, 2012 – February 1, 2013
Anwesha is the annual techno-cultural fest of Indian Institute of Technology Patna. It is a four-day-long event usually held towards the end of January every year. The fest hosts technical, cultural, literary, eco and management events.The first edition of Anwesha took place in 2010.It draws a footfall of about 4,500 from more than 140 colleges across the country.
Automatic Electrical Defibrillation
May 1, 2012 – September 1, 2012
Electrical defibrillation is the most effective way to treat the ventricular tachycardia (VT) and ventricular fibrillation (VF). An automatic external defibrillator based on DSP is introduced in this project. The whole design consists of the signal collection module, the microprocessor controlingl module, the display module, the defibrillation module and the automatic recognition algorithm for VF and non VF, etc. This automatic external defibrillator has achieved goals such as ECG signal real-time acquisition, ECG wave synchronous display, data delivering to U disk and automatic defibrillate when shockable rhythm appears, etc.
Sai Teja Residency
May 1, 2010 – December 1, 2011
Worked with a broad range of Company’s data resources, collaborated with finance and product management team as a quantitative analyst, working closely with business owners to understand business challenges and provide data/analysis for better decision making. Optimized the usage of the area and remodeled the plan as to achieve a better premium price per square-foot leveraging the remodeled plan.
Biomedical Image Analysis in Python
DataCamp
June 24, 2026 – Present
Text Analytics - Level 2
IBM
June 24, 2026 – Present
Data Science for Business - Level 2
IBM
June 24, 2026 – Present
Deep Learning
IBM
June 24, 2026 – Present
edX Verified Certificate for Querying with Transact-SQL
edX
June 24, 2026 – Present
Machine Learning
Coursera Course Certificates
June 24, 2026 – Present
Intermediate R - Practice
DataCamp
June 24, 2026 – Present
Intermediate R
DataCamp
June 24, 2026 – Present
Intro to Python for Data Science
DataCamp
June 24, 2026 – Present
Intro to Statistics with R: Introduction
DataCamp
June 24, 2026 – Present
Introduction to R
DataCamp
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, spanning crime analysis, real estate optimization, customer insights for various industries (banking, insurance, wealth management), and even engineering simulations, demonstrates a broad intellectual curiosity and adaptability. Their experience in both large corporations (IBM, Oracle) and startups (Autosure.ai, Traffk, Data Cult) suggests an ability to thrive in different organizational cultures. The mentorship roles also indicate a willingness to share knowledge and contribute to a learning environment. The target role of 'Data Analyst' aligns well with the candidate's core competencies in data analysis, modeling, and insight generation, although their experience leans more towards a 'Data Scientist' profile.
Soft Skills & Operational Fit
The candidate's experience as a mentor at Udacity and leadership roles (Head of Data Science, Chief Data Scientist) suggest strong communication, leadership, and team management skills. The project descriptions indicate an ability to collaborate with cross-functional teams (finance, product management, client engineers) and translate business challenges into data-driven solutions. The focus on understanding business challenges and providing data/analysis for better decision-making aligns well with operational fit for a senior Data Analyst role.