Dallas, Texas, United States
Data Scientist/ ML Engineer with over 5 years of professional experience across global organizations like Tata Consultancy Services, Google (via Cognizant), and Microsoft (via Mindtree), specializing in data analysis, anomaly detection, predictive modeling, and business intelligence. Adept at leveraging SQL, Python (Pandas, NumPy, PySpark), Power BI, and Tableau to transform raw data into actionable insights that drive decision-making and operational efficiency. Proven expertise in developing automated ETL pipelines, ML-powered anomaly detection systems, and Gen AI-driven, customer-facing analytics solutions. Strong background in cloud services (AWS, Azure, GCP), NoSQL databases (MongoDB), and production monitoring. Hands-on experience with LLM-based and agentic AI systems, including RAG pipelines, NLP techniques and tool-orchestrated multi-step workflows. Passionate about building scalable data solutions with a solid foundation in machine learning, MLOps, and responsible AI practices. Experienced in cross-functional collaboration, stakeholder communication, and Agile methodologies.
• Experienced in querying and analyzing data using SQL and Python to support business decisions, investigation, and resolving data issues from external systems, and designing future applications. • Developed interactive, real-time Power- BI dashboards to visualize trends, monitor KPIs, and enable data-driven decision-making across stakeholders. • Extensive experience in the onsite-offshore working model, managing project timelines through negotiation with business owners, ensuring effective delivery under tight deadlines, estimating effort requirements, providing technical training, mentoring team members, and facilitating knowledge sharing. • Served as a Team Lead, overseeing a team of 10 members, skilled in conducting code and specification reviews, performing integration testing, ensuring smooth system launches and troubleshooting application failures. • Experienced in Agile environments with hands-on expertise across all phases of the software development lifecycle, including requirement gathering, coding, testing, implementation, and support. • Successfully improved click-through rates (CTR) by 50% by implementing personalized email campaigns, driving higher user engagement as a key performance indicator (KPI), meeting and exceeding KPIs for email engagement and campaign effectiveness through data-driven insights and optimizations. • Designed and deployed a customer-facing chatbot leveraging GPT, LangChain and RAG (Retrieval-Augmented Generation) to deliver real-time responses for vehicle inquiries, service scheduling, and product support, seamlessly integrated with Stellantis’ customer portal. • Developed FastAPI endpoints in Python to handle user queries from the chatbot interface, retrieve relevant context using RAG pipelines, route inputs through LangChain agents to GPT, process the LLM response, and return the result back to the frontend for real-time user interaction.
Worked with clients across diverse industries, including: 1. Stellantis (Fiat-Chrysler Automobiles) – Automotive 2. Citi Bank – Financial Services 3. ACI Worldwide – Payments & Banking Technology As a Lead Data Scientist/ ML Engineer at Tata Consultancy Services, I led impactful data projects for global clients including Stellantis, Citi Bank, and ACI Worldwide. I managed a 10-member team, developed automated ETL pipelines, built interactive Power BI dashboards, and implemented ML-based anomaly detection frameworks. I also deployed a customer-facing chatbot using GPT, LangChain, FastAPI, and RAG systems to enhance real-time user interaction. My work enabled data-driven decision-making, operational efficiency, and improved customer engagement across industries.
•Conducted data ingestion, data pre-processing and data analysis using pandas and NumPy, defined and monitored KPIs for the project, contributing to data- driven decision-making. • Designed and implemented anomaly detection frameworks using Python OOP principles and unsupervised techniques like the Interquartile Range (IQR), reducing data discrepancies by 75% and ensuring higher data integrity. Developed and deployed MLOps pipelines to automate anomaly detection and forecasting models, enhancing operational efficiency. • Developed and maintained Python-based data ingestion and analytics pipelines using frameworks like pandas, numPy and PySpark, ensuring efficient and reliable data flow. • Automated daily data tasks by leveraging shell scripting, Python and SQL, including, and optimizing data extraction workflows in Treasure Data platform and converting shell scripts to Python, enhancing productivity, and minimizing errors. • Experienced with NoSQL techniques using MongoDB for data extraction and python libraries for analysis, optimized database performance, and performed ETL processes to extract, transform, and load data, contributing to streamlined data integration. • Utilized Jira to manage project tasks, track sprint progress, and coordinate with cross-functional teams in Agile environments, ensuring timely delivery of data analytics and reporting solutions. • Experience working in a production environment with multiple applications, scheduling, and monitoring Cron jobs with multiple integrations on servers. • Monitoring Production system to ensure Quality of Service for customer deliverables and enhancing customer experience and responsiveness.
• Analyzed and reconciled large datasets from payment processing systems, identifying patterns and anomalies to support fraud detection and operational improvements. • Built anomaly detection scripts using statistical thresholds and machine learning techniques (e.g., Isolation Forest, z-score analysis) to proactively flag suspicious activity. • Demonstrated strong analytical acumen by uncovering key business trends through advanced SQL queries and Python (Pandas, NumPy), enabling data-driven decision- making across departments, reducing false positives and improving fraud detection accuracy. • Designed and developed interactive dashboards and visualizations using Power BI, enhancing executive visibility into key KPIs like transaction success rates, customer churn, and system performance. • Automated recurring reporting processes using Python scripts and SQL stored procedures, reducing manual effort by 40% and improving report accuracy. • Created executive-level presentations and reports to communicate complex analytical findings in a clear, concise, and impactful manner to senior stakeholders. • Collaborated cross-functionally with product, finance, and operations teams to gather business requirements and translate them into data insights that drove strategic initiatives.
Responsible for providing accurate information and help resolve technically challenging Cognitive and Machine Learning issues for clients incorporating Microsoft Azure APIs in business models. • Detecting bugs and reporting for improved SDLC to Windows Azure cloud platform (IaaS and PaaS) and Azure CLI. • Responsible for engaging with customers for defining the customer facing technical problems and solutions using customer evidence, guiding product development with customer feedback. • Responsible for resolving client issues related to various Azure cognitive AI-powered services (being called through SDK, Rest API or Studio) such as Form recognizer, Face recognition, Computer vision, Bing custom search, QnA maker, speech recognition, spell check, Language understanding etc. Met process slas, productivity and quality targets within the organization.