Bridging Machine Learning Rigor with Commercial Strategy.
I am a Data Scientist currently pursuing an MBA, positioning at the intersection of AI Product Management (Technical / AI PM) and Senior Applied AI / ML Engineering.
My engineering foundation is grounded in Digital Signal Processing and statistical rigor at Manipal Institute of Technology (MIT), battle-tested delivering +$12M in premium adjustments with compound GLMs at EXL Service, and elevated through graduate business education focusing on tech strategy and unit economics.
Rigorous grounding in stochastic systems, Fourier analysis, discrete filter design, mathematical optimization, and statistical inference.
Curriculum focused on Tech Product Strategy, Unit Economics, GTM Execution, Market Discovery, and Financial Valuation.
The Journey & Leadership Timeline
A deliberate progression from deep mathematical modeling to commercial product leadership.
Hands-on Data Scientist & Systems Builder
Rooted in Electronics & Communication (DSP Minor) at Manipal Institute of Technology (MIT). Shipped production actuarial GLM systems recovering +$12M annually and containerized BERT NLP pipelines on Google Vertex AI at EXL Service.
- ▸Frequency-Severity statistical models & Expected Audit Value matrices
- ▸Sub-100ms BERT transformer inference on Vertex AI endpoints
- ▸On-device YOLO computer vision & localized IoT telemetry appliances
MBA Candidate: Tech Strategy & Unit Economics
Bridging technical execution with commercial acumen. Deepening capabilities in AI product discovery, unit economics, market sizing, financial modeling, and go-to-market (GTM) execution.
- ▸Inference cost vs. latency trade-off modeling and cloud unit economics
- ▸Product Requirement Documents (PRDs) and user feedback loops
- ▸Enterprise stakeholder alignment & regulatory auditability frameworks
AI Product Management & Technical Strategy
Operating as the bilingual translation layer between machine learning engineering teams and executive leadership. Converting ambiguous business problems into rigorous, high-ROI AI architectures.
- ▸Translating commercial goals into measurable ML loss functions & metrics
- ▸Prioritizing AI roadmaps by customer impact and compute feasibility
- ▸Leading high-velocity cross-functional engineering and design pods
Technical & Strategic Arsenal
Form Follows Function.
The best machine learning systems are not the most complex—they are the ones stripped of vanity ornamentation, calibrated for high-throughput reliability, and wired directly into measurable commercial outcomes.