About Me

Over the years, I have built deep expertise in Marketing and Customer Data Science, delivering suites of predictive and prescriptive machine/deep learning and statistical models (both Frequentist and Bayesian paradigms) — ranging from churn and purchase propensity, attribution models to generosity sensitivity scoring, and even automated case-note triaging. I have designed, delivered, and deployed company-wide experimentation platforms. Most recently, I’ve been providing thought leadership in state-of-the-art Reinforcement Learning, Causal Inference, and pioneering MLOps capabilities, including Feature Stores.

Earlier in my Post-doctoral Position, I developed a physically motivated, unsupervised clustering algorithm in Python, trained on data from the GAMA Galaxy Redshift Survey [1]. This algorithm refined galaxy distance (redshift) measurements by associating them with their corresponding galaxy clusters.

Similarly, during my doctorate I studied and used the kinematic properties of the Milky Way and discovered that its total weight is under a trillion solar masses!! The research papers highlighting this results have since earned gratifying citations worldwide [2] and [3].

Outside of academia, my interests have grown across three core areas of Data Science:

• Machine Learning and LLM Ops: End-to-end ML Model Delivery from Scoping to Productionisation. Agentic AI frameworks and orchestration.
• Experimentation Ops: Statistical and Commercial Significance, A/B Testing, Causal Inference.
• Scientific Excellence Ops: Tie Model Delivery Life Cycle to the CRISP-DM formalism.

I primarily code in Python, love to contribute to open-source Data Science initiatives, and actively support internal shared codebases.

All words expressed in this blog, including diary entries, are my own and reflect my personal views and biases—they do not represent the opinions of my current or past employers. This blog is licensed under the MIT License.