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AI and Data Science Leader | Agentic AI, GenAI and ML Products | Reinforcement Learning
I am an AI and Data Science leader with 14+ years of experience building machine learning products, leading data science teams, and turning complex business problems into production AI systems. My work spans AI strategy, ML product development, roadmap planning, stakeholder partnership, and hands-on delivery across Agentic AI, GenAI, forecasting, recommendation systems, computer vision, NLP, and production machine learning. I operate at the intersection of technical leadership, product thinking, and business impact, leading initiatives from problem framing to scalable deployment. Over the years, I have led teams and delivered analytical and AI solutions for organizations and clients including Google, Smollan, Abu Dhabi Government, Yes Bank, and Accenture. These initiatives have covered customer intelligence, retail forecasting, recommendation engines, computer vision systems, knowledge graphs, NLP solutions, enterprise AI platforms, and intelligent automation. I enjoy building and mentoring data science teams, shaping practical AI roadmaps, partnering with business leaders, and turning technical possibilities into products that create measurable business value. Alongside my industry work, I continue to explore reinforcement learning, Agentic AI, and emerging intelligent systems, with a focus on connecting research ideas to practical real-world applications.
Stanford Continuing Studies
Data Analysis
January 1, 2015 – January 1, 2015
Rajasthan Technical University
Bachelor of Technology (B.Tech.), Mechanical Engineering
N/A – Present
Argility
Sr. Staff Data Scientist
April 1, 2023 – Present
Majid Al Futtaim
Staff Data Scientist
November 1, 2022 – April 1, 2023
Remote
Xebia
Principal data scientist
August 1, 2020 – November 1, 2022
Xebia
Lead Data Scientist
December 1, 2018 – August 1, 2020
Data Scientist (Google Retail) (Accenture)
September 1, 2017 – November 1, 2018
Data Scientist (Google Customer Marketing)
October 1, 2016 – August 1, 2017
Mahashiv Promoters
Data Science Manager
December 1, 2011 – September 1, 2016
Gurgaon
App Store Analysis Package in R and Python
February 1, 2017 – Present
Computerized the way towards finding the unordinary design (increment/diminish) in the time arrangement alongside certain different bits of knowledge created next to each other. Discovered Correlation between different markets and use highly correlated markets to predict the counterfactual of an individual market using Bayesian Structural time-series model. Investigated how the response metric would have evolved after the intervention if the intervention had never occurred. Some operation that the package is currently able to summarized swiftly :- Highlight the aberration in Time series. Plot Significant %Change Over Time Plot Insignificant %Change Over Time Extract data where changes are Significant/Insignificant Extract data where %Compounded Changes are Significant/Insignificant Plot or Extract data by leaving out desired number of days and mention cutoff_value threshold for aberration identification
Attribute Indexing Model
February 1, 2017 – Present
Created indexes for the most commonly used attributes which also performs comparative analysis for pre and post optimization period and validate the level of significance in the difference of results arise. Analysis around how long apps see a spike in pageviews and downloads after a feature release. Analysed the behavior of Page views, downloads and conversions after a feature release. If there is a spike after release, predict will cover how long does it stays. Approach includes looking for the variances in page views and downloads a week before the launch by calculating the running variance of the post period for different combinations of post period.By discovering the time when the variance comes closer to the pre period the stability of series can be decided.
Google Small Business Customer Segmentation
October 1, 2016 – February 1, 2017
Performed an unsupervised segmentation to help advertiser acquire and retain customer cost effectively. Built algorithm to automatically assign new customer to the segmented category that included the use of machine learning techniques such as extended t-SNE, Deep neural decision tree etc. Examined different results and impact of new advance machine learning technique to segment customer such as Density-based spatial clustering of applications with noise (DBSCAN). Codes were developed from scratch to perform varieties of statistical checks
White Ops - Document Machine Learning Platforms
May 1, 2016 – Present
A terse, straightforward documentation on how to use various platforms for machine learning problems.
Big Data Analysis and Reporting on Speeding and Red Light Camera Offences
May 1, 2016 – June 1, 2016
Performed Analysis Using OLAP Operations to discover pattern over time and presented seasonal patterns in types of Offences. Finding out the tendency towards more (or less) driving by the motorists over Time.Investigate the effect of direction of travel and zone on the number of offences and value of Fine imposed.Phases of Project involved Data understanding, Gathering suitable data from other reliable sources in case it is missing and then performing data cleansing operation followed by data analysis.Representing result in tabular and graphical form.
Stanford University Mini Project
August 1, 2015 – September 1, 2015
Graphic evolution to the P&L month by month to see if there are any particular month where we took hits or gains and get a good lay of the land Find out which financial operations cost the most and Look at the portfolios of strategies that worked the best Tell the total P&L impact by currencies. Are there currencies or zone that caused more issues than other?
Predictive Modelling and Social Media Analysis-Towne Center
March 1, 2015 – June 1, 2016
Performed Sentiment Analysis of Social Media data and procured various tweets / posts trends . Explored networks and tried to gain insights about their importance. Build predictive model to explore the impact on sale by various advertisement media. Database Creation Using SQL for organizing events and planning Developing routines for Data Extraction and Loading Working on building a platform to perform automated business intelligence and benchmarking Strategic and Market Analysis with latest Research including Economic Outlook, Industry analysis, Customer Profile and Competitive Analysis Model Building and Policy Construction to ensure maximum revenue, optimum profitability and minimum risk Preparing for Model deployment with continuous monitoring and update Customer Segmenting and analysing competitor strategies. Considering Social Media Use and customer movements to forecast customer participation. Working on optimization of shopping points including their size and location of warehouse associated with them Interacting with customers/Clients for daily progress report or requirement at the site to ensure the smooth running of the project as per schedule
Project Planning and Quality Analysis of Shopping Mall
January 1, 2013 – March 1, 2015
Responsible for preparing project plan along with the estimation of material, manpower, budget and fixation of time targets Improved Quality and Productivity by the use of efficient development techniques, standards and guidelines Created sustainable model for generating timely view into usage and revenue data Regularly charting out project timelines and tried on minimizing forecasting error. Analysed the industries or markets in to understand and communicate market proven best practices and benchmark targets that can be implemented internally Developed tactical plans to prepare early stage opportunities for pursuit. Developed time lines for preparation, identified teaming requirements, identified hurdles, risks and plans mitigation of the same. Performed Six sigma strategies and worked on collecting and analysing data for concrete designing, process mapping and Root Cause Analysis Performed Trend tracking and cash flow analysis
Project Planning and Quality Analysis of Water Treatment Plant
December 1, 2011 – November 1, 2013
Helped trades people interpret the job's designs, and ensured the job is constructed according to the project plans Assisted project controls including budgeting, project scheduling and forecasting Ensured the execution of projects are in accordance with a valid, executed contract, as per company policies and procedures. Ensured optimal product quality and service Oversaw raw materials inventory working with vendors to communicate quality and delivery issues. Facilitated numerous root cause failure analysis investigations in regard to safety and equipment failures. Daily operations of field work activities and organization of subcontractors Work instructions for customized and standardized plant Coordination of the implementation of a project, ensuring it is being built correctly. Review of engineering deliverable Regular project status reports; budget monitoring and trend tracking Effective communications between engineering, technical, construction, and project controls groups
Google Cloud Certified Professional Data Engineer
June 24, 2026 – Present
Data Science: Data to Insights
MIT Professional Education
June 24, 2026 – Present
Tame Data to Drive Big Insights
Stanford University
June 24, 2026 – Present
The Analytics Edge Via Edx
Massachusetts Institute of Technology
June 24, 2026 – Present
Statistical Learning Mooc
Stanford University
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a diverse project portfolio, including personal projects and significant contributions at major companies like Google, Xebia, and Majid Al Futtaim. The roles held (Sr. Staff Data Scientist, Principal Data Scientist, Lead Data Scientist) demonstrate a progression and increasing responsibility in data science and ML, aligning well with a senior ML Engineer's growth trajectory. The breadth of applications (retail, banking, customer marketing, computer vision) suggests adaptability and a willingness to tackle varied challenges. However, the lack of explicit open-source contributions or community involvement makes it difficult to fully assess cultural fit beyond professional roles.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight leadership in data science teams, strategic thinking in evaluating AI trends, and problem-solving through custom algorithm development. These indicate strong operational fit and soft skills relevant for a senior ML Engineer role, including collaboration, mentorship, and strategic influence.