I am grateful for the opportunities I have had. Here’s a quick summary.

Leadership and Team Building

I’ve led companies and teams through successful data journeys - from excel based reporting to modern BI tech stacks to data science engines.

Built analytics, data engineering, and data science teams. Mentored teams on design philosophies, approaches to analytics, data science, and other technical skills.

Advised several start-ups and mid-sized organizations on data-driven strategies.

Established roadmaps for thought leadership for companies and professionals.

Build data infrastructure mapping out the entire customer journey bringing together digital analytics platforms, customer care platforms, paid media platforms, and social platforms.

Managed and trained teams across the globe.

Private Equity, Start-Ups, and M&As

I’ve been actively involved in several private acquisitions - on behalf of acquiring companies and target companies.

Led analytics and finance efforts on M&As, amounting to more than $350 million in investments.

Developed enterprise valuations with EBIDTA multiple analyses for companies.

Forecasted growth multiples in the digital marketing, retail, and employee benefit spaces.

Advised several start-ups on infrastructure, business strategy, and exit strategies.

Nurtured start-ups from ideation to product and go-to-market.

Coached entrepreneurs on launch strategies, data strategies, and market pivots.

Bespoke Data Platforms and Infrastructure

I’ve had the chance to build bespoke data platforms and infrastructure for the different companies and clients I have worked for:

  • Analytics and BI infrastructure

  • Data Science and Machine Learning Platforms

  • Bespoke Customer Data Platform

    • Web Analytics, Digital Ad Platforms, Social Media Platforms, Email Marketing, Customer Advocacy

Projects and ML Portfolio

I’ve had the chance to work on a wide variety of data projects - all the way from setting up infrastructure to ingest, transform, and model data, to building BI reports for insights to building machine learning models to improve organizational bottom lines.

Most recent Data Science and Machine Learning projects include:

  • Sales potential index/algorithm – Predicts the cohort of potential customers for the best ROI

  • Predictive Fraud Alert Systems – An algorithm that detects fraudulent sales transactions and provides a program health score

  • Customer Advocacy and NLP – A model to understand customer grievances by aggregating and classifying customer reviews

  • Customer Activity Prediction - Predicts the customer’s most likely activities based on customer demographics and historical engagements

  • Win back Model - Predicts canceled members who are most likely to come back

  • Predictive and Corelative studies

  • Customer Care/Call Center optimizations

  • Sales Forecasting

Tools and Systems

I know my way around these tools and platforms:

  • Web analytics - Google Analytics UA and Google Analytics 4

  • Paid media - Google Ads and Microsoft Ads, Zeta, Funnel I/O

  • Paid Social - Facebook/Instagram Insights, Twitter, YouTube, TikTok, LinkedIn

  • Customer care - Talkdesk, InContact and FreshDesk

  • Email communication - HubSpot, SendGrid, and Iterable

  • CRM Platforms - Salesforce, SugarCRM

  • Conversation platforms -Invoca

  • Surveys Platforms - Satmetrics, QuestionPro

  • Back end tach stacks - Microservices, ERPs, APIs, Webhooks