Summary
Overview
Work History
Education
Skills
Certification
Other Responsibilities
Timeline
Generic
Alok Ranjan

Alok Ranjan

Assistant Manager (Data Science & AI)
Bangalore

Summary

Experienced Data Science Lead with 8+ years of expertise in data science and AI, specializing in machine learning (ML), generative AI, and deep learning (DL) to solve complex business challenges. Demonstrated success in developing innovative applications and leading data science initiatives across industries such as retail, banking, insurance, and ESG. A dedicated leader and collaborative team player, committed to streamlining processes and effectively communicating data-driven insights. Eager to leverage expertise in ML/DL, generative AI, and AI agents to deliver impactful, business-focused models.

Overview

8
8
years of professional experience
4
4
Certifications

Work History

Assistant Manager (Data Science & AI)

KPMG
09.2017 - Current
  • Product Recommender Tool Using Gen AI: Developed a Generative AI-powered recommendation tool for the Sales team. This application intelligently suggests the most relevant products from an extensive inventory of 40,000 items based on user inputs and specifications. By analyzing user requirements and preferences, the tool provides tailored recommendations that enhance the sales process, enabling the team to quickly and effectively meet customer needs. This innovation streamlines decision-making, boosts sales efficiency, and improves customer satisfaction.
  • Extensible Knowledge Accelerator (EKA): Designed and developed the Extensible Knowledge Accelerator (EKA), an advanced multimodal Retrieval-Augmented Generation (RAG)-based Generative AI system. This tool empowers junior employees by providing intelligent knowledge assistants, significantly reducing the learning curve and increasing productivity. The EKA enables junior data scientists to deliver tailored client solutions with minimal code modifications, supporting faster onboarding and enabling scalable growth in response to business demand.
  • Insurance Claim Fraud Detection System: Built a machine learning model using client’s historical data to detect fraud in health insurance claims. The system accurately distinguishes between legitimate and fraudulent claims, improving fraud detection efficiency for a financial services client.
  • In-House Financial Risk Scoring Application: Led the research, proposal, and development of a comprehensive in-house financial risk scoring application for DASH and Audit teams. The solution enabled seamless data ingestion, scoring, and integration with firm assets, replacing third-party tools, cutting subscription costs, and providing greater control over pricing. It processes general ledger data, classifying transactions into high, medium, or low risk using control points such as Benford’s Law, detection of complex structures, expert rules, high-value transactions, manual entries, anomalies, unusual amounts, unbalanced entries, etc. This initiative enhanced risk management, improved internal audit capabilities, and drove efficiency and cost savings.
  • Lead Management Ecosystem: Developed an analytics-driven lead management ecosystem for a leading finance company to classify customers by purchase propensity for various insurance products. The project boosted cross-selling across channels, improving policy density, average ticket size, and customer lifetime value. It also optimized agent attrition, reduced business leakages, and improved headcount planning during expansions. The product recommendation engine and cross-sell propensity model achieved high conversion rates, empowering the sales force with personalized campaign strategies. This initiative increased persistency, CSAT scores, and overall campaign effectiveness while refining go-to-market strategies.
  • Wind Turbine Fault Diagnosis System: Addressed client challenges in remotely located wind turbines with limited fault diagnosis and preventive maintenance techniques. Manually labeled 4,000 turbine blade images as damaged or non-damaged, with damage types such as vortex generator issues and broken teeth. Developed a Deep Convolutional Neural Network (DCNN) to classify blade images, accurately identifying damaged versus non-damaged blades and specifying damage types. The model achieved 92% detection accuracy across the top five defect categories, significantly improving fault diagnosis and maintenance efficiency.



Education

Bachelor of Engineering - IS&E

Visvesvaraya Technological University
09.2016

Skills

Data Science: Machine Learning, Deep Learning, Generative AI, Agentic AI, NLP, LLMs, Deployment

Certification

DP-100: Microsoft Certified – Azure Data Scientist Associate

Other Responsibilities


  • Manage a team of 7 members, overseeing performance, project allocation, and professional development to ensure productivity and team growth
  • Coordinate project proposal planning and execution, balancing workloads and aligning tasks with individual strengths to meet deadlines and business objectives
  • Foster a collaborative environment by promoting open communication, regular feedback sessions, and continuous learning

Timeline

Assistant Manager (Data Science & AI)

KPMG
09.2017 - Current

Bachelor of Engineering - IS&E

Visvesvaraya Technological University

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Alok RanjanAssistant Manager (Data Science & AI)