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Data Analytics Leader | Experimentation & Personalization | Causal Inference & Incrementality Measurement | ML- Driven Decision Systems
I’m a data science and marketing analytics leader with 10+ years of experience, operating as a player-coach —leading teams while staying hands-on in building experimentation-driven decision systems that translate complex data into measurable business impact. My recent work focuses on experimentation & measurement, incrementality analysis, and portfolio optimization—building ranking and decision policies that determine what to show, when to show it, and to whom, while rigorously measuring true incremental value. Most recently, I led a randomized portfolio ad placement optimization experiment, ranking products from an acquired and acquiring company by expected NPV to drive in-app ad sequencing, and measured an ~4% lift in incremental realized value through test-control experimentation. Across roles, I’ve delivered impact at scale, including: ◼️ Driving multi-million-dollar annualized value through experimentation-led personalization and portfolio optimization initiatives ◼️ Improving marketing addressability and response through propensity and cross-sell models, expanding eligible customer populations by double-digit percentages ◼️ Supporting regulatory-facing analytics and model monitoring for large consumer credit portfolios, partnering with business and risk teams to address audit and compliance requirements I’ve led and mentored teams, collaborated closely with product and engineering, and helped organizations move from intuition-driven decisions to experiment-validated strategies grounded in clear metrics and causal reasoning. I’m especially interested in work involving marketing analytics, experimentation platforms, incrementality measurement, ad ranking and sequencing, portfolio optimization, and causal inference. Open to conversations around experimentation, incrementality measurement, and decision systems leadership. #MarketingAna
The University of Western Australia
Doctor of Philosophy (Ph.D.), Biomedical/Mechanical Engineering
January 1, 2011 – January 1, 2015
Massachusetts Institute of Technology
Master's Degree, Computational Science
January 1, 2005 – January 1, 2006
Indian Institute of Technology, Roorkee
Bachelors of Technology, Civil Engineering
January 1, 2001 – January 1, 2005
EXL
Engagement Manager (Decision Analytics)
October 1, 2018 – Present
New York, United States · On-site
Novantas
Senior Software Engineer (Data Science)
March 1, 2017 – June 1, 2018
New York, New York · On-site
Infosys
Lead consultant (Data Science)
October 1, 2015 – March 1, 2017
Englewood/Hartford, USA · On-site
MARRS Professional Services, Inc
Programmer Analyst
February 1, 2015 – September 1, 2015
Plainsboro, New Jersey
Metis Data Science Academy
Data Science Fellow
January 1, 2015 – January 1, 2015
New York City, NY · On-site
The University of Western Australia / National University of Singapore
Researcher
July 1, 2007 – December 1, 2014
Greater Perth Area · On-site
Generative AI for Beginners
Microsoft
June 24, 2026 – Present
Certified Data Scientist
Metis
June 24, 2026 – Present
Feature Engineering for Machine Learning
Udemy
June 24, 2026 – Present
Deep Learning A-Z™: Hands-On Artificial Neural Networks
Udemy
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a diverse background spanning research, consulting, and industry roles, indicating adaptability. The project descriptions show a focus on delivering tangible business value and working with various client types (CPG, healthcare, banking). The transition from civil engineering to computational science and then data science demonstrates a strong learning orientation. However, the target role is 'Software Engineer' while the experience is heavily skewed towards 'Data Science' and 'Analytics Management'. While there are mentions of building ML pipelines and modernizing systems, the core software engineering depth (e.g., specific programming paradigms, system architecture beyond ML pipelines, robust software development practices) is not explicitly detailed, which might impact cultural fit for a pure software engineering role.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight leadership in data science engagements, problem-solving through model development, and strategic thinking in identifying risk and optimizing processes. These indicate strong analytical and operational capabilities. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is not possible.