About

I am Data-powered strategist with experience in analytics, management consulting, product and program management, I thrive in the dynamic space where leadership meets execution, seamlessly bridging the gap between strategy and results. With close to ~10 years of experience at Google, McKinsey, and Infosys, I've honed my skills in leading and collaborating with diverse teams, navigating complex stakeholder landscapes, and driving impactful outcomes through data-driven insights and collaborative approaches. Whether it's crafting GTM strategies for product/ sales activation, flawlessly executing programs, or building trust with senior stakeholders across functions, I bring a unique blend of strategic thinking, analytical rigor, and relationship-building expertise to the table. My passion lies in unlocking potential – both within teams and across organizations – through effective communication, shared vision, and a commitment to continuous improvement. Skillset summary: Data-driven decision-making, Business process design and execution, Data analytics, ML/ AI, Product/ program management, C-level alignment & influence, senior/ cross functional stakeholder management, Data storytelling, agile problem solver.

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Skills

Experience

Strategy and Operations Manager

Google

Feb-2022 to Present
Advisory Committee Member, Strategic AI Program

CERTIFi by Mercy University

Jan-2024 to May-2024
Advisory Council Member

Harvard Business Review (HBR)

Dec-2023 to May-2024
Junior Engagement Manager

McKinsey & Company

Sep-2020 to Jan-2022
Senior Risk Consultant

McKinsey & Company

May-2018 to Aug-2020
Data Scientist

CUNA Mutual Group

Feb-2018 to May-2018
Data Analyst Intern

CUNA Mutual Group

Jun-2017 to Dec-2017
Test Engineer(Data Quality Analyst)

Infosys

Sep-2014 to Jun-2016
System Engineer

Infosys

Oct-2013 to Aug-2014
Intern

Infosys

Feb-2013 to May-2013

Education

UConn School of Business

MSc in Business Analytics and Project Management

Passout Year: 2017
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I.K.Gujral Punjab Technical University (IKGPTU)

B.Tech in Computer Software Engineering

Passout Year: 2013

Peer-Reviewed Articles

Industrial Relations at Enterprise Level: A Case Study

Industrial relations in India have witnessed a long journey, from the phase of industrialisation to independence era to the age of economic reforms, but a lot needs to be addressed given the role of cordial industrial relations in desired economic growth of a country. The key lies in recognition of workforce as an essential and integral part of the organisation and not merely a tool for the production. The second National commission on labour recommended consolidation of labour laws, given that there are numerous labour laws both at the state level and the central level. Most of the labour laws are applicable to organisations employing a given number of workers, mostly ten or more workers. In order to escape the laws, organisations have been using contract labour more, so as to avoid the constraints of hiring and firing in adjusting to production demands. This study concentrates upon how the broad structure of specific patterns of industrial relations operates in a given context, and thereby provide an insight for solving a number of emerging problems. It helps to locate where the problem is, which may require modifications in the structural patterns. This provides an objective analysis to which those responsible for managing industrial relations in industry can relate their own experiences and can help them to seek avenues of change in realising their industrial relations objectives.

Projects

Oct-2016 to Present

Red Hat Business Value

Built a classification model which helped to predict the customer potential based on their online activities and the characteristics of the people. Extensive use of machine learning algorithms like ensemble models, Decision tree, Logistic Regression etc. were implemented to help Red Hat’s marketing products target towards these valuable customers.
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Sep-2016 to Present

Predictive Modeling Project- Lending Loan Club

Extensive Data cleansing process was performed to prepare the data for modeling. Data Preprocessing techniques like handling missing values, Principal Component Analysis, Outliers Analysis, Transformations and Multivariate Analysis was performed using SAS JMP.
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Scholar9 Profile ID

S9-052024-2801358

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