MLDS speaker · 2026

Rajesh Parvathini

Partner AIMLTiger Analytics

About Rajesh Parvathini

Rajesh Parvathini is a seasoned AIML leader with over 17 years of cross-industry experience spanning Retail, CPG, Insurance, Oil & Gas, QSR, and Banking. Operating at the intersection of deep technical rigor and commercial strategy, he currently serves as a Partner in AI/ML at Tiger Analytics. In this role, he leads a multidisciplinary team of data scientists, machine learning engineers, and application developers to build next-generation solutions that deliver measurable value to the clients. Throughout his career, Rajesh has solved multi-dimensional problems across industries. His portfolio of projects includes geospatial optimization for oil drilling, anomaly detection for security data science, real-time intent detection for call centers, and personalized product and promotional recommendations for the Retail and QSR sectors. His current focus is reframing classical data science problems within autonomous, agentic frameworks. A cornerstone of Rajesh’s leadership philosophy is mentorship, where he focuses on fostering a culture of continuous learning and technical excellence within his teams. An alumnus of the National Institute of Technology (NIT), Warangal, with a B.Tech in Electronics and Communication Engineering, Rajesh has built a distinguished career that includes impactful roles at Optum, Millward Brown, and Inductis.

1 edition on the MLDS stage · 1 session on record

Rajesh Parvathini, Partner AIML, Tiger Analytics

Talks at MLDS

Sessions from the MLDS agendas, newest first.

MLDS 2026
Bengaluru
26–27 Mar 2026
- Mar 26, 2026 · 15:45–16:05 · Hall 1 · Bengaluru
  Keynotes / Tech Talks
  Reframing Classical Data Science Solutions within an Agentic AI Framework
  Traditional revenue management and marketing science models are increasingly throttled by Human-in-the-Loop bottlenecks and search space explosion where the exponential growth of variables (cross-elasticity, competitor tactics, and channels etc.) and the number of models outpaces manual oversight. Static ML workflows simply cannot scale when every scenario requires human-led iterative corrections. This session explores the agentic shift where classical ML models are wrapped within a reasoning loop. By treating models as "Tools" rather than final outputs, LLMs act as a central "brain" to decompose high-level goals into executable plans. These systems utilize long-term memory to retain historical context and self-correcting loops to autonomously refine model parameters or flag data anomalies. Unlike isolated model outputs that require human intervention to stay relevant, the autonomous system can perform diagnostic sub-routines, run multiple what-if and optimization scenarios, and automate modeling activities at scale.
MLDS 2026 agenda MLDS 2026 talk recordings on YouTube (opens in a new tab)