Oct 2024 – Apr 2025 · 6 mos · Bengaluru, Karnataka, India · On-site

• Director- advisory role focused on shaping advanced analytics, forecasting, and GenAI solutions for global clients.
• 1. Advised CXO-level stakeholders across industries on building AI/ML capabilities, defining analytics roadmaps, and integrating GenAI into business strategy.
• 2. Led the design and prototyping of advanced forecasting and GenAI modules as part of enterprise product modernization – including commodity price prediction, demand decomposition, NL2SQL, and OCR pipelines.
• 3. Built executive influence and cross-sector visibility, gaining exposure to CPG, Q-Commerce, Manufacturing, and Procurement domains through high-impact client engagements.
• 4. Architected AI solutions leveraging LLMs (RAG, OCR, Translation) to automate processes and drive cost savings, improving solution time-to-value for multiple verticals.
• 5. Integrated PoC learnings into reusable frameworks, laying the foundation for scalable analytics accelerators and product IP.

Mar 2023 – Oct 2024 · 1 yr 7 mos · Bengaluru, Karnataka, India · Hybrid

• As Head of Data Science at UPL, I led the strategic vision and execution of AI initiatives, focusing on transforming data into actionable insights for the agricultural sector.
• Key Responsibilities:
• 1. Strategic AI Leadership: Directed AI and data science strategies to align with business goals, addressing key challenges in agriculture. Managed a hybrid team of data scientists and MLOps professionals, emphasizing recruitment, mentorship, and a culture of innovation.
• 2. AI Project Management: Oversaw large-scale AI projects in demand forecasting and manufacturing optimization, ensuring seamless integration into business processes. Partnered with various departments to prioritize and deliver impactful data science projects.
• 3. GenAI Development: Led the creation of Generative AI applications, enhancing demand forecasting and customer engagement. Established frameworks for monitoring AI model utilization and ROI, demonstrating tangible business impact.
• Key Achievements:
• 1. Improved Demand Forecasting: Implemented AI models that enhanced forecasting accuracy and optimized inventory management.
• 2. Operational Efficiency: Optimized manufacturing processes, resulting in increased productivity and cost savings.
• 3. Recognition: Received accolades for the value added through innovative AI applications and data-driven decision-making.
• Tools & Technologies: Python, Azure Databricks, MLFlow, Airflow, Azure OpenAI, LangChain, RAG (Retrieval-Augmented Generation)

Jun 2022 – Mar 2023 · 9 mos · Bengaluru, Karnataka, India · Hybrid

• At Lam Research, I was responsible for managing and delivering advanced data science solutions focused on improving manufacturing processes in the semiconductor industry. My work in image processing and deep learning significantly enhanced defect detection and classification on silicon wafer images, driving operational efficiency.
• Key Highlights:
• 1. Defect Detection & Clustering for Semiconductor Manufacturing: Developed deep learning models, including YOLOV5 and XceptionNet, for detecting and classifying defects (scratches, stains, dust) on silicon wafers. These models improved defect detection accuracy by over 85%, reducing manual inspection efforts.
• 2. Process Optimization through AI: Provided advanced analytics and process optimization solutions for various business units, contributing to cost savings and enhanced manufacturing efficiency.
• NLP-Based Supplier Quality Insights: Applied topic modeling using the BERTopic library to analyze supplier quality datasets, identifying key quality issues and improving supplier management.
• 3. Technical Leadership & Cross-Unit Collaboration: Led AI projects across business units, ensuring timely delivery and integration into existing workflows. Provided technical guidance to teams, enabling the successful implementation of cutting-edge data science methodologies.
• Tools & Technologies: Python, TensorFlow, SQL, Azure Synapse, Azure Databricks, SAP HANA, Power BI, OpenCV

Dec 2019 – Jun 2022 · 2 yrs 6 mos · Bengaluru, Karnataka, India · Remote

• I was responsible for driving AI/ML and advanced analytics initiatives in the marketing domain, focusing on optimizing marketing spend and improving customer insights. I led a team of data scientists and analysts, delivering high-impact solutions that transformed the way Philips approached market forecasting and campaign optimization.
• Key Highlights:
• 1. Market Share Forecasting: I developed a predictive system using time series models and machine learning (ML) techniques to provide early warnings on competitive market strategies. This system achieved over 90% accuracy in predicting extreme changes in market share, allowing the business to better allocate resources and respond to market dynamics.
• 2. Marketing Mix Modeling (MMM): I led the development of a Bayesian statistical model for optimizing global marketing spend across the EU and North America markets. This model enabled Philips to determine channel ROIs, helping marketers plan future media investments more effectively.
• 3. Automated Analytics Framework: I architected a reproducible and auditable analytics framework to monitor machine learning models across the marketing function. This cloud-agnostic solution streamlined the execution of marketing models, ensuring efficient lifecycle management.
• 4. Stakeholder Management: Worked closely with senior marketing leaders to translate complex AI/ML insights into actionable business strategies, ensuring alignment with business goals.
• Key Achievements:
• 1. Optimized Global Marketing Spend: The MMM model I developed enabled Philips to optimize marketing spend, leading to more efficient resource allocation and improved campaign performance.
• 2. Data-Driven Marketing Strategy: The AI/ML models helped Philips gain deeper insights into customer behaviour and competitive actions, significantly enhancing decision-making in marketing strategies.
• Tools & Technologies: Python, TensorFlow, SQL, Azure Synapse, Power BI, Bayesian Modeling, Shapley Values

Mar 2018 – Nov 2019 · 1 yr 8 mos · Bengaluru, India & Bensheim, Germany

• In this role, I led a team of data scientists and business analysts, focusing on solving complex supply chain challenges using AI/ML and advanced analytics. My efforts resulted in significant improvements in operational efficiency, cost optimization, and demand forecasting accuracy across TE Connectivity's global supply chain.
• Key Responsibilities:
• 1. Led AI/ML initiatives focused on demand forecasting and inventory optimization, reducing forecasting errors by 30% and improving overall supply chain performance.
• 2. Developed descriptive analytics dashboards that provided real-time insights into supply chain KPIs like delivery performance and inventory levels, driving strategic decision-making.
• 3. Managed end-to-end AI/ML project delivery, collaborating with cross-functional teams and senior leadership to align solutions with business goals.
• 4. Recruited and mentored a high-performance data science team, fostering a culture of collaboration and innovation.
• Tools & Technologies: Python, SQL, SAP Business Objects, Azure Machine Learning, Tableau, Power BI, R

Aug 2006 – Mar 2018 · 11 yrs 7 mos · Bengaluru, Karnataka, India · Hybrid

• At Quest Global, I led AI/ML projects across various international locations, including India, Spain, the United States, the Czech Republic, Egypt, and Saudi Arabia:
• 1. Predictive Modeling - Condition-Based Monitoring (CBM): Developed algorithms for predictive maintenance of mechanical components using sensor data. This involved data engineering, model development, and deployment, along with descriptive analytics to extract insights from historical data. Worked primarily from Bengaluru, India. Tools: MATLAB, SQL, regression models.
• 2. Advanced Analytics for Direct Material Productivity: Delivered data-driven insights for edge devices, leveraging descriptive analytics to optimize operations and enhance decision-making. This role included project management and customer interaction in the U.S. (Nevada).
• 3. Model-Based Development (MBD): Managed data analytics projects involving MBD, hardware-in-the-loop (HIL), and software-in-the-loop (SIL) testing for system validation, with teams located in India and Spain.
• 4. Modeling & Simulations for FMCG: Conducted simulation modelling and engineering support for packaging operations across the Czech Republic, Egypt, and Saudi Arabia.
• Technical skills applied include Python, MATLAB, predictive maintenance, machine learning, statistical modeling, descriptive analytics, data engineering, and project management across these global assignments.