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Senior Technical Lead(Data Science) | Computer Vision · ADAS · Generative AI | Mercedes-Benz R&D | IIT Kanpur
Senior Technical Lead & Product Owner at Mercedes-Benz R&D India, with 11 years building scalable AI/ML, computer vision, and cloud-native systems for industrial and automotive applications. Currently architecting and building a GenAI-powered ADAS Validation Copilot — a LangGraph-based multi-agent system combining NL→SQL reasoning, RAG-based scenario retrieval, and local LLM inference, enabling validation engineers to query and diagnose KPI results in natural language — reducing manual triaging and accelerating diagnostic visibility across large-scale multi-modal validation datasets. I bridge deep technical execution with product ownership: translating complex validation challenges into measurable, data-driven pipelines. My work spans agentic AI systems, KPI evaluation frameworks, scenario extraction, and VLM-based usecase. M.Tech in Signal Processing from IIT Kanpur. Recipient of multiple process innovation and excellence awards, including a 2025 Department Team Award for the ADAS Stack Evaluation Framework.
Indian Institute of Technology, Kanpur
Master of Technology (M.Tech.), Signal Processing and communication
January 1, 2013 – January 1, 2015
Silicon Institute of Technology
Bachelor of Technology (B.Tech.), Electronics and telecommunication
January 1, 2008 – January 1, 2012
J.N.V. Nuapada
10 +2
January 1, 2006 – January 1, 2008
Mercedes-Benz Research and Development India
Senior Technical Lead(Data Scientist)
September 1, 2022 – Present
Bangalore · On-site
Atkins
Lead Data Scientist
July 1, 2022 – August 1, 2022
Atkins
Senior Data Scientist
November 1, 2018 – June 1, 2022
Sasken Technologies Limited
Senior research Engineer (machine learning and data science)
October 1, 2016 – November 1, 2018
bangalore
HT Media Ltd
Software Developer
July 1, 2015 – September 1, 2016
Noida Area, India
M.Tech thesis on "On Techniques to detect malicious users in cooperative spectrum sensing"
July 1, 2014 – June 1, 2015
1) In this thesis a proximity based classification method is proposed to detect malicious user in cognitive radio network, which does not require an estimate of number of outliers. 2) GOF based cooperative sensing technique is proposed using AD and KS test and later DS theory is applied to combine the result of both the tests. This technique outperforms the previously proposed methods. 3)
Convex optimization to improve Cramer Rao Bound and maximum likelihood estimation
January 1, 2014 – June 1, 2014
1) A lower bound was achieved on mean square error in estimating a deterministic biased vector. 2) Modified the Maximum Likelihood estimator by linear transforms and achieved MSE lower than CRB. 3)Demonstrated and compared the performance of standard ML estimator with linearly transformed biased estimator and affine transformed biased estimator.
VLSI, VHDL and MOS layout
Central Tool Room & Training Centre
June 24, 2026 – Present
Machine learning of Stanford University by Andrew Ng.
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has worked in diverse industries (automotive, engineering consulting, telecom, media) and on a variety of projects, from ADAS validation to resource optimization and predictive maintenance. This breadth of experience suggests adaptability and a willingness to tackle different problem domains, which generally indicates a good cultural fit for dynamic environments. The progression through senior roles also points to a commitment to growth and leadership.
Soft Skills & Operational Fit
The candidate's experience as a Senior Technical Lead and Lead Data Scientist suggests strong leadership, project management, and problem-solving skills. The descriptions of leading teams and owning product delivery indicate good operational fit for roles requiring initiative and responsibility. However, without specific psychometric test results, a detailed assessment of stress handling or team collaboration is not possible.