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AI Security & Responsible AI Specialist | Agentic AI | Data Scientist with 12 Years of Experience
Jay comes with 11+ years of professional experience in data science and analytics across different domains like FinTech (payments, banking, lending, insurance etc.) Currently, he works as a Lead Data Scientist at a leading global bank, applying AI, ML, and GenAI to build smarter, more secure, and efficient systems — from cyber defense copilots to AI governance frameworks. Prior to this, he worked as a Principal Data Scientist for Blenheim Chalcot India, wherein he lead R&D on Generative AI to develop digital products to solve business problems for internal portfolio companies. Prior to this, was associated with Pelican.ai to solve problems in the compliance and fraud management lifecycle for financial institutions primarily in the UK & EU region. 𝗞𝗲𝘆 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄: - Adept in identifying user problems, formulating hypotheses and implementing solutions through Machine Learning & Deep Learning techniques. - Technical skill set includes fluency in Python and R. - Usage of Machine Learning spans across several fields like Predictive Analytics, Forecasting, NLP and LLMs. - Well versed with handling database like MySQL, MongoDB and cloud services like AWS and Azure. - Worked across several stages of Data Transformation, Data Pipeline building and have successfully build Data Solutions across several Businesses. 𝗔𝗿𝗲𝗮𝘀 𝗜 𝗵𝗮𝘃𝗲 𝘄𝗼𝗿𝗸𝗲𝗱 𝗮𝗰𝗿𝗼𝘀𝘀 𝗮𝗿𝗲:- 1. Predictive Modeling - Churn prediction, Customer Segmentation, Delinquency rate prediction, Time Series Forecasting, Outlier detection using unsupervised ML, Implemented Deep Learning algorithm in businesses. 2. NLP - Chatbots, Information Retrieval, Aspect based Sentiment Analysis, Information Extraction from text, worked extensively with LLMs, RAG and AI Agents. 3. Others - Databases - MySQL, MongoDB, CloudServices -
University of Mumbai
Bachelor of Technology - BTech, Computer Science
January 1, 2011 – January 1, 2014
University of Mumbai
Post Graduation Diploma in Applied Statistics (PGDASS), Applied Statistics
N/A – Present
Banking Sector
Lead Data Scientist - AI Security
January 1, 2025 – Present
Hybrid
Blenheim Chalcot
AI Solutions Architect (Principal Data Scientist)
November 1, 2023 – December 1, 2024
London Area, United Kingdom · Remote
Pelican.ai
Lead Data Scientist - NLP
July 1, 2021 – November 1, 2023
London Area, United Kingdom · Hybrid
ICICI Lombard
Product Manager - Data Science
December 1, 2018 – July 1, 2021
Mumbai Area, India
JPMorgan Chase & Co.
Senior Data Scientist- Text Mining & NLP
December 1, 2017 – December 1, 2018
Mumbai, Maharashtra, India
Freelance
Data Science Mentor
January 1, 2017 – December 1, 2018
Hybrid
Maersk Group
Machine Learning Engineer
November 1, 2015 – December 1, 2017
Mumbai Area, India
Fiverr
Freelance - Data Scientist
May 1, 2015 – December 1, 2018
Remote
Hansa Cequity
Data Scientist - Customer & Marketing Analytics
July 1, 2014 – October 1, 2015
Mumbai Area, India
Agentic Security Operations Analyst
March 1, 2025 – July 1, 2025
An AI-powered assistant designed to support Security Operations Center (SOC) teams in handling alerts more effectively. What it does: - Acts as an AI Co-Pilot by ingesting alerts from SIEM and enriched data from SOAR - Translates raw logs into natural language summaries, making incidents easier and faster to interpret - Provides best-practice investigation guidance to analysts for each alert type - Helps reduce alert fatigue, improves consistency, and minimizes false positives The result: Faster investigations, reduced workload for analysts, and stronger overall cyber defense posture.
Agentic Bot for Customer Support
October 1, 2024 – November 1, 2024
- Improvised customer service with advanced conversational bots that handle complex support tasks, emulating human interaction to provide a seamless experience. - Implemented for an EdTech company to boost learner engagement and streamline support, addressing user interaction challenges effectively. - Projected to improve customer resolution rates by 35% and cut query handling time by threefold, all while maintaining existing operational costs.
Voice AI Agents for Automated Collections
September 1, 2024 – October 1, 2024
- Developed a pilot for Oakbrook Finance leveraging LangChain Agents to optimize customer call timings based on interaction history, enabling virtual agents to handle automated monthly collections seamlessly. - Delivered a fully integrated solution using Twilio for telephony, Eleven Labs for Text-to-Speech, OpenAI Whisper for Speech-to-Text, and LangChain with RAG framework, enabling efficient virtual call handling. - Projected to replace 40% of entry-level tele-calling agents, driving significant cost savings and improving the efficiency of customer communication.
Fraud Detection on Inbound Transactions
March 1, 2024 – July 1, 2024
➢ Created an ML-powered model for a UK Fintech (Modulr Limited) to detect fraud, accurately flagging suspicious transactions. ➢ Designed an automated, end-to-end pipeline to streamline feature engineering and switch seamlessly between model linear and non-linear algorithm types for optimal results. ➢ Achieved a 95% success rate in identifying fraud and a 65% Value Detection Rate within the top 5 percentile.
Dynamic Clustering for Customer Segmentation
April 1, 2023 – August 1, 2023
• Led and guided a team in developing DynamicSegment, an innovative end-to-end solution focussed on end-to-end clustering to create dynamic customer segmentation. • Spearheaded R&D efforts at various stages of product development to enhance clustering accuracy and effectiveness. • Orchestrated seamless data integration, preprocessing, and transformation techniques to optimize clustering performance • Made significant contributions to the field of customer segmentation by advancing the understanding and application of clustering techniques, ultimately improving marketing effectiveness and customer satisfaction.
Financial Health Check for Credit Assessment
January 1, 2023 – May 1, 2023
• Spearheaded a critical project focused on analyzing historical statement data to assess income-expense patterns, including disposable income, existing liabilities, next pay date, and potential missed payments, for bank customers. • Employed advanced data analysis techniques and statistical modeling to derive meaningful insights and accurately evaluate the creditworthiness of customers. • Developed a robust feature that enabled banks and credit agencies to gain a comprehensive understanding of their customers' financial patterns, aiding in more informed credit decisions and risk management. • Demonstrated a deep understanding of financial concepts and regulatory requirements, ensuring the analysis adhered to industry standards and best practices. • Played a pivotal role in enhancing the overall credit assessment process, enabling institutions to make more accurate and informed lending decisions, mitigate risk, and improve customer satisfaction.
Trade Digitization - Entity Extraction Model
November 1, 2022 – March 1, 2023
• Led a high-performing team in the successful development of a product aimed at extracting entities pertaining to goods (field 45) and documents (field 46) from SWIFT 7XX Messages. • Implemented state-of-the-art techniques using Python's SpaCy and RASA frameworks, ensuring accurate and efficient entity extraction with a performance recall of 70% & precision of 95%. • Orchestrated the final deployment of the model as a robust and scalable service, leveraging REST API technology for seamless integration with existing systems. • Collaborated closely with stakeholders to understand and address their specific requirements, resulting in a highly tailored and impactful solution. • Contributed significantly to enhancing trade digitization efforts, streamlining processes, reducing manual effort, and improving overall efficiency.
Intelligent Forecasting and Decision Support System
October 1, 2021 – March 1, 2023
• Architected and implemented a powerful Generalized Forecasting Engine capable of automating the entire forecasting process, from data reading to feature engineering, feature selection, model development, and selection of the most optimal model as the final forecast. • Streamlined the forecasting workflow by integrating advanced techniques for data preprocessing, including feature engineering and feature selection, reducing manual effort and enhancing accuracy. • Designed the engine to be versatile, allowing both internal financial models and end customers to leverage its capabilities for revenue forecasting from multiple fronts and forecasting their financial behavior, respectively. • Significantly contributed to enhancing forecasting accuracy, enabling financial models and end customers to make more informed decisions based on reliable and comprehensive forecasts.
Automated Transaction Categorization
August 1, 2021 – March 1, 2022
• Led a transformative project centered around transaction categorization, leveraging advanced NLP algorithms and machine learning models to accurately extract relevant entities and classify transactions based on their nature and purpose. • Developed a system capable of categorizing transactions into multiple income and expense heads such as Travel, Food, Grocery, Salary, and more. • The product aimed to empower end customers to make informed financial decisions by providing them with a comprehensive understanding of their spending and income patterns over different time periods, such as the past week, month, or year. • Contributed significantly to enhancing financial awareness and enabling customers to proactively manage their finances, promoting better budgeting and financial well-being.
MoBuyLytics
November 1, 2016 – July 1, 2017
- The aim was to perform an exploratory research and analysis on the current supply-demand trends of mobile industry - Primary data collected from mobile phone users via online-offline survey, while secondary data (mobile phone features) collected via web scraping using Python (pandas) for over 1000+ mobile phones - Performed statistical techniques like Factor analysis, Hypothesis testing, ANOVA, Clustering etc. using Minitab & SPSS. - Worked on data science algorithms like Decision Trees, Logistic Regression, Classification etc. on R Studio. - Currently working on developing a Mobile Recommender Model, that aims to assist the consumers in their mobile purchase decision.
Equipment Tracking - Data Quality Improvement Project
November 1, 2016 – November 1, 2017
- The project focussed on analyzing the equipment (container) tracking process and eliminating the data quality errors generated during the container journey. - Working closely with the core operations and provide analytical deliverables such as dashboards, statistical analysis, insights generation and preparing a plan to transform insights into action. - Developed (i) Error-Sequence model using apriori algorithm, (ii) Text Mining on Error-Cause-Solution data and presented it in workshops conducted at Rotterdam, Netherlands - Aug’17 and Shanghai, China - Feb’17 respectively. - Initiated the project with a baseline of 14.76% errors, and enabled the business to reduce it to 10.36% within 12 weeks.
Maintenance Data Analysis (Cranes) - APM Terminals
January 1, 2016 – March 1, 2016
- Analyzing the Preventive, Corrective, Breakdown & Planned maintenance data for cranes at APM Terminals (Maersk Group) and providing descriptive analysis for performance evaluation of various cranes. - The maintenance data was unstructured with open ended text format for Error, Cause and Solution parameters - Text Mining techniques were used in R Language to transform the unstructured data into usable form; and then hierarchical clustering was performed to identify the relationship between Errors, Causes & Solutions. - The business insights from this data were used to support the data driven intelligence of the stakeholders in order to improve the efficiency & efficacy of cranes. - Currently working on Predictive Analytics on cranes which includes, 1. Anomalies Detection 2. Proactive Failure Detection 3. Risk Analysis 4. Preventive maintenance etc.
Recommender Model
March 1, 2015 – March 1, 2015
- Resort Recommender Model for a hospitality industry which helps the client to promote low occupancy resorts to a specific target group of members who are most likely to visit that resort, thereby improving the occupancy of problem resorts. - Developed on open-source R platform using Collaborative Filtering Technique.
Lead Nurturing and Propensity Model
December 1, 2014 – December 1, 2014
- Leads Propensity Model was developed in open source R language using Logistic Regression technique for a hospitality industry. - The objective was to score the leads on the basis of their attributes to identify which leads have higher probability to convert into members.
Validation of Toll Pass at Toll-Plaza using RFID
June 1, 2013 – March 1, 2014
- This project focuses on automatic validation of toll pass using radio frequency identification (RFID) technology. - The system automates the toll collection process in a way, such that a customer does not have to stop and pay cash at a toll booth thus reducing the transaction time of the customer and also eliminating financial errors in the process. - The project was developed using Visual Basic as the front end and MS Access as the back end.
Content Based Image Retrieval (CBIR)
June 1, 2010 – March 1, 2011
- Content-Based Image Retrieval (CBIR) is the application of computer vision to solve the problem of searching for digital images in the large multimedia databases - Content-Based means that the search will analyze the actual contents of the image such as colors, shapes, textures and any other information that can be derived from the image itself
Certified Agentic AI Developer
Blockchain Council
June 30, 2026 – Present
Certified Artificial Intelligence (AI) Expert
Blockchain Council
June 30, 2026 – Present
CutShort Certified Machine Learning (ML) - Advanced
Cutshort
June 30, 2026 – Present
Operations Analytics - University of Pennsylvania
Coursera Course Certificates
June 30, 2026 – Present
Microsoft Certified: Azure AI Fundamentals (AI 900)
Microsoft
June 30, 2026 – Present
Machine Learning in Production
DeepLearning.AI
June 30, 2026 – Present
CutShort Certified R Programming - Basic
Cutshort
June 30, 2026 – Present
Business Analytics and Data Mining Championship 2018
SAS
June 30, 2026 – Present
R Programming
Coursera
June 30, 2026 – Present
Introduction to the Internet of Things and Embedded Systems
Coursera
June 30, 2026 – Present
CutShort Certified Python - Advanced
Cutshort
June 30, 2026 – Present
Big Data Foundations - Level 1
IBM
June 30, 2026 – Present
The Data Scientist’s Toolbox
Coursera
June 30, 2026 – Present
Cultural Fit Analysis
The candidate's extensive experience across various industries (banking, venture building, finance, insurance, logistics, marketing) and roles (Lead Data Scientist, AI Solutions Architect, Product Manager, ML Engineer, Mentor) demonstrates adaptability and a broad perspective. The diverse project types, from security operations to customer support and financial modeling, indicate a willingness to tackle varied challenges and a strong problem-solving orientation. The focus on delivering business value and leading teams aligns well with a senior-level role requiring both technical depth and strategic thinking. The certifications in AI and ML further show a commitment to continuous learning and staying current with industry trends.
Soft Skills & Operational Fit
The candidate's experience as a Lead Data Scientist and AI Solutions Architect, coupled with mentoring roles, indicates strong leadership, team management, and communication skills. The project descriptions highlight collaboration with stakeholders and a focus on business value, suggesting good operational fit and ability to translate technical solutions into business impact. The freelance experience also points to entrepreneurial drive and client management capabilities.