Greater Bengaluru Area
Hands on authentic leadership experience in Data Science and AI field with ideating, developing and operationalising numerous ML products in my career of 20 years in the domains related to NLP, CV, DL and Forecasting at high-growth startups, unicorns and global giants. 10+ years as a DS team Lead, people manager, and Director, I am mentoring, evaluating and coaching on career growth with empathy and growth mindset! l have learned that true engineering leadership isn't just about scaling headcount; it’s about maximising value under constraints. Currently, I lead a high-impact lean data science team, focusing on audience measurement globally. My approach is built on three pillars: 1. Strategic Lean Management: Delivering Nx output by leveraging AI and Disciplined Capacity Management. I thrive in cost-conscious environments where efficiency is the primary driver of innovation. 2. Operational Resilience: Building engineering cultures that are—stable, predictable, and aligned with long-term business goals. 3. The GCC Advantage: Navigating the complex intersection of Global corporate vision and Indian engineering and AI talent to build world-class Global Capability Centers. I am passionate to solve increasingly complex DS problems at scale within a reasonable economic means. Hire, Build, coach and retain the talent is what drives my leadership style. I believe that AI/ML can solve sophisticated business challenges and I am looking forward to create high influence and impact on org using AI/ML. I believe the next decade of tech belongs to leaders who can balance fiscal discipline with technical excellence. Whether I am architecting a system or a team, my goal is the same: Sustainable, high-margin growth. One of my key professional learnings came from the first Covid period about the importance of shock resistance mechanism within the AI models. No one could foresee a sudden drop in sales and customers and jeopardising the whole statistics of the features making the operationalised model outputs useless and counter productive. I learned that, continuous monitoring, feedback loop, retraining strategies and adoption metrics design are key to a sustainable model. We build next set of models which showed amazing resilience for next Covid waves, economic and other external shocks by adopting these key elements.Outside of tech, I am a firm believer in the "High-Performance Lifestyle"—maintaining the physical and mental discipline required to lead in high-stakes environments. 📍 Based in Bangalore | Avid Read & Writer | AI Mentor
Nielsen is a global leader in audience measurement which includes traditional TV viewing and new age content providers such as Streaming and small publishers platforms. I am leading the Data Science effort for Digital audience measurement across various publishers, platforms and geographies. I am doubling my expertise about an engineering leadership that maximizes value under constraints. Currently, I lead a high-impact lean data science team, focusing on audience measurement globally. My approach is built on three pillars: 1. Strategic Lean Management: Delivering Nx output by leveraging AI and prioritizations in a cost-conscious environments where efficiency is the primary driver of innovation. 2. Operational Resilience: Building engineering cultures that are—stable, predictable, and aligned with long-term business goals. 3. The GCC Advantage: Navigating the complex intersection of global vision and Indian engineering and AI talent to build world-class GCC.
1. Architecting the AI and ML solutions for very diverse set of application catering to numerous business units. Collaborating and aligning with Data offices, Security and governance teams and BU to build innovative and useful AI applications. 2. Orchestrating a sustainable AI/ML work group for large scale Data science programs simultaneously executed with efficiency and effectiveness. Creating and managing processes that helps sailing the organization through technological disruptions and change managements such as LLM/GenAI. 3. Hands on authentic, Strategic and thought leadership in building Data Science and analytical products, operationalizing for multiple years of Global impact for 100+ countries and varied business lines with the collaboration in multi-stakeholder environment. 4. Hiring, Building, Leading, coaching and retaining data science team of high impact to foster the Schneider's vision of digital and automation transformation using AI and ML.
(A Jio company) 1. Leading and mentoring a team of high performance data scientist and engineers for Search and discovery/ recommendation/Chat bot/Multi Lingual NLP task/ Computer vision. 2. I am my team is breaking down above problems into individual ML and engineering problems and solving it using Lexical and Semantic language and CV understanding leveraging ES, Solr, SOTA on NLP/CV and Knowledge graph. 3. Helping Product managers to build awesome data products and Data Scientists to build useful models to improve Learning outcomes of students for Indian academics and competitive exams.
1. Solving customer malady one order at a time. Leading the Trust/safety/CC charter by building ML/AI models from structured/ unstructured data at scale. 2. DS Models - Customer fraud models, Cash on delivery block models, ETA prediction, Order pipeline anomaly models, Chat bot automation model and food intelligence models. 3. ML tools and Tech - Classification and Regression models using GBT and NN, Feature Engineering, Analytics, AB Tests, Stratified Samplings, BERT and Word2Vec based Language Model, Uplift/ Propensity Models based TOT/4T
(Brocade became the part of Broadcom) - Problems related to large network : Devising data driven solutions, finalising data requirement and EDA, evaluating ML and statistical approach, prototyping and making it production ready. - LSTM based RNN Auto Encoders to find hidden dependency and pattern among multivariate time series. - Using Graph NN such as Node2Vec and GCN based methods on the data emitted by thousands of devices in a graph of network to derive the embeddings for security and inventory management use cases.
Finding suitable methods and make them production ready for data emitted by thousands of networking devices in tandem. Modelling of Uni-variate and multi-variate Time series, Forecasting behaviour of network parameters and Anomaly Detection to solve the inventory/ Bad devices and malicious activity in the network. This involves understanding of the statistical patterns of wide verities of time series and finding suitable methods to model them.