Sridhar Kulkarni

Results-driven Senior Data Scientist with nearly 10 years of experience in designing and delivering advanced analytics solutions while building data science capabilities for multinational corporations. Expertise in Python, machine learning, SQL, Microsoft Azure, generative AI, and LLM technologies has facilitated the successful execution of projects that align technical innovation with strategic business objectives. Proficient across diverse industries including supply chain, automotive, retail, construction, engineering, and nuclear, consistently delivering actionable insights that optimize operations, enhance customer engagement, and drive measurable business impact. Recognized for mentoring teams and architecting scalable frameworks, bringing a unique blend of technical depth, strategic vision, and leadership to every engagement.

Senior Lead Data Scientist (AtkinsRealis)

• Engineered a sophisticated SQL Chatbot leveraging advanced prompt engineering and Python programming to enable conversational querying of enterprise SQL Server data.
• Designed and implemented a multi‑agent Agentic Framework with specialized agents:
  Planner Agent – interprets user queries, decomposes tasks, and dynamically assigns them to the right agents.
  SQL Agent – generates and executes optimized queries with schema grounding and validation.
  LLM Agent – handles natural language understanding, contextual reasoning, and query refinement.
  Reporting & UI Agent – delivers insights through interactive dashboards and user‑friendly interfaces.
• Empowered business users to access insights directly, reducing reliance on technical teams and accelerating decision‑making cycles.
• Defined roadmap for dashboard integration and enterprise‑wide scalability, ensuring adaptability across diverse projects.
• Implemented secure query validation and error‑handling pipelines to safeguard enterprise data integrity.
• Technologies: Python, SQL Server, Azure OpenAI GPT, Streamlit, Copilot Studio Agents, Enterprise Data Management

• Designed and implemented an AI-powered solution to automatically generate Project Management Plans (PMPs) by extracting and structuring information from Contracts, RFPs, and Business Management System (BMS) documents.
• Leveraged Azure OpenAI GPT to parse unstructured text and populate standardized PMP templates, ensuring compliance with PMI guidelines.
• Reduced manual effort for managers and stakeholders by accelerating PMP preparation time by 70%, enabling faster project kickoffs.
• Integrated with enterprise document repositories to ensure seamless ingestion of project artifacts and real-time updates.
• Developed custom prompt engineering workflows to improve accuracy of section mapping (scope, schedule, risk, communication, procurement).
• Technologies: Azure OpenAI GPT, Microsoft Copilot Studio, Python, Streamlit, Enterprise Document Management Systems

• Architected an internal HRM/PM application using Microsoft Azure Copilot Studio combined with pro‑code solutions in Python and LLMs.
• Enabled HRs, Project Managers, and Hiring Managers to interact with a Copilot Studio Agent for JD parsing, candidate shortlisting, and scoring.
• Finalized Copilot Studio Agent solution ensuring scalability and seamless integration with HRMS and enterprise tools.
• Improved recruitment efficiency by 50%, reducing manual JD parsing and candidate evaluation time.
• Technologies: Azure Copilot Studio, Python, LLMs, Streamlit

• Developed an AI‑driven assistant on Microsoft Azure Copilot Studio with pro‑code extensions to support Business Managers in navigating the RBMS.
• Finalized Copilot Studio Agent solution, enabling decision‑makers and new employees to gain rapid business understanding and improve onboarding efficiency.
• Delivered conversational insights tailored to regional operations, enhancing strategic decision‑making.
• Reduced onboarding time by 40%, accelerating knowledge transfer for new employees.
• Technologies: Azure Copilot Studio, Python, LLMs, Streamlit

• Designed and implemented an OCR‑based validation solution to process and verify PDF files against multiple business rules.
• Built a custom extraction model using Microsoft Azure Document Intelligence to label and prepare data for downstream validation.
• Developed Python scripts to extract JSON outputs, clean, and restructure data for rule‑based checks aligned with business requirements.
• Leveraged Azure Function Apps, Blob Storage, and CI/CD pipelines to deliver a scalable, end‑to‑end solution within enterprise Azure architecture.
• Ensured robust automation and compliance for nuclear industry documentation workflows, reducing manual validation errors by 60%.
• Technologies: Python, Azure Document Intelligence, Azure Function Apps, Azure Blob Storage, APIs

STP & LTP (Spare Parts Demand Forecasting)

• Developed advanced time series forecasting models to predict demand for millions of spare parts across global service centers.
• Ensured availability of critical parts while optimizing warehouse and storage utilization to minimize costs.
• Applied Supply Chain Management and Demand Planning techniques to improve operational efficiency and customer satisfaction.
• Reduced inventory holding costs by 20% while maintaining service level agreements.
• Technologies: Python, SQL Server, MS Azure Data Science, Time Series Forecasting

CJM (Customer Journey Management)

• Designed and implemented analytics solutions to map and analyze customer behavior patterns across digital channels.
• Delivered behavioral segmentation and recommendation models to support sales teams in guiding customers through the purchase journey.
• Increased digital engagement by 35% through personalized recommendations and optimized sales pathways.
• Technologies: Python, SQL Server, MS Azure Data Science, MS Azure DataBricks, Website Behavior Analysis

NexAction – Cross-Sell & Upsell Analytics Platform

• Designed and implemented machine learning models to identify whitespace opportunities for cross-sell and upsell across customer portfolios.
• Built a scalable analytics platform integrating Python, R, SQL Server, Elastic Search, and Streamsets for data ingestion, transformation, and visualization.
• Leveraged Azure ML Services and ML Studio to develop, train, and deploy predictive models in production.
• Delivered actionable insights that enabled sales teams to target high‑potential opportunities, improving customer engagement and driving 15% revenue uplift.
• Technologies: Python, R, SQL Server, Elastic Search, Streamsets, Azure ML Services, ML Studio