プロフィール詳細

プロジェクトを作成

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Lokesh C.に依頼

India

Data Scientist & Analyst | ML & Gen AI | Technical Writer and Editor | 7+ years of experience | Business Consulting

プロフィール概要

Senior AI/ML Engineer & Solutions Architect specializing in Generative AI (LLM/RAG), time-series forecasting, and document intelligence for supply chain, logistics, and operations. I have majorly been in Largely been in FMCG, Health-Tech, fintech, manufacturing domain I build secure, cloud-native analytics platforms on AWS that turn messy operational data into decisions for delivering faster access, higher accuracy, and measurable cost impact. Signature Projects-: 1) GenAI Logistics Assistant (LLM/RAG over shipments & warehouse data) An enterprise LLM assistant that lets ops teams ask natural-language questions over governed shipment, warehouse, and inventory data—returning verifiable answers with lineage and audit trails. Stack: Python, FastAPI, Bedrock, ECS, Redshift, S3, Docker, CI/CD, RBAC, auditability, prompt-safety guardrails, vector search. Impact: ~80% reduction in data-access latency; traceable and governed analytics for ops and leadership. 2) Demand Forecasting Overhaul (SAP-IBP, TS/ML, MLOps) A production forecasting framework that segments items, tunes models automatically, and monitors bias/accuracy—integrated with planning workflows and executive KPIs. Methods: ARIMA/SARIMA/Holt-Winters, XGBoost, segmentation, bias/override controls, KPI dashboards (MAPE/WAPE/RMSE). Impact: ~12% accuracy lift, ~5% bias reduction, 45% fewer overrides across multiple BUs. 3) 3D Dimensioning + Pallet Optimization (Ops research + cloud microservices) A computer-vision + optimization solution that captures carton dimensions, simulates pack patterns, and recommends optimal pallet builds inside DC operations. Stack: API-first microservices on ECS; constraint/rotation handling; integration with DC workflows. Impact: $20–30M annual savings across 40+ distribution centers; improved pallet utilization; cost-to-serve reduction. 4) Forecast Engine Utility (CPG, multi-region, production MLOps) A standardized forecasting service for thousands of SKUs/customers, with pipelines, experiment tracking, and quality guardrails to support regional rollouts. Scope: 5k+ SKUs, 1k+ customers; data warehousing, pipelines, monitoring & model registry, A/B tests. Impact: Regional scale-out with stable MLOps, observability, and SLA adherence. 5) Contract & Document Intelligence (NLP/CV/IDP) An intelligent document processing pipeline that extracts entities, classifies documents, and enables semantic search for contracts and compliance artifacts. Stack: OCR (Tesseract), NER (spaCy/NLTK), OpenCV, semantic/fuzzy search, document classification & extraction, compliance analytics. Impact: Lower per-request cost; faster, explainable compliance decisions. 6) AgentifyCA — Modular AI SaaS (Tech Lead; audit & reconciliation) A modular back-office platform for accountants that automates document intake, reconciliation checks, and audit reviews with role-based access and privacy controls. Focus: Document automation, reconciliation, audit workflows, secure RBAC, multi-format ingestion (PDF/CSV/Excel), privacy-aware design. Impact: Unified back-office operations; accelerated onboarding to AI-assisted checks and robust review trails. Platforms & Subject-Matter Expertise 1. Cloud, Data & Architecture: AWS (Bedrock, ECS, Redshift, S3, Lambda, API Gateway, IAM, VPC), Docker, CI/CD, API-first microservices, ETL/ELT, data governance, observability, cost optimization. 2. GenAI & Retrieval: LLM apps, RAG, embeddings, vector databases, prompt engineering, guardrails, safety, auditability & traceability, PII/privacy controls. 3. Planning & Supply Chain: Demand forecasting, inventory/MRP analytics, network & logistics analytics, DC operations, KPI frameworks, bias/override management. 4. NLP/CV & IDP: OCR/NER, document intelligence, contract analytics, semantic/lexical search, document classification/extraction, knowledge retrieval. 5. Analytics & BI: Python, SQL, pandas/numpy/scikit-learn, Power BI/Tableau, A/B testing, experiment design, KPI definition, executive dashboards. Stats work for Research Papers 1. Literature & Synthesis: PRISMA-style screening, rapid evidence maps, decision-grade summaries. 2. Reproducible Analytics: Clean notebooks/pipelines, EDA, diagnostics, effect sizes, data dictionaries. 3. Methods: TS/ML comparisons (SARIMA/ARIMA, GBMs/XGBoost), segmentation studies, power analysis, A/B test design. 4. NLP/CV Studies: Corpus curation, tokenization/feature engineering, OCR+NER for regulatory/market analysis, retrieval evaluation. 5. Communication: Technical writing for execs and reviewers; appendices (tests, code, assumptions) for transparent peer/board review.

専門分野

• ★ Data Analysis
• ★ Machine Learning
• ★ Technical & Scientific Writing
• ☆ Data Science
• ☆ Predictive Modeling & Analytics
• ☆ Data Processing
• ☆ Data Visualization
• ☆ Data Manipulation
• ☆ Data Mining
• ☆ Deep Learning
• ☆ Trend Analysis
• ☆ Python
• ☆ Tableau
• ☆ Data-Driven Decision Making

サービス

• Writing
• Research
• Consulting
• Data & AI
• Product Development

Writing Copywriting

Research Market Research, Feasibility Study, Fact Checking, Gray Literature Search, Systematic Literature Review, Secondary Data Collection

Consulting Scientific and Technical Consulting

Data & AI Statistical Analysis, Algorithm Design-Non ML, Data Visualization

Product Development Formulation, Concept Development

職務経験

Engineering Lead

Felicity Strategic Advisors Pvt. Ltd

1月 2025 - 現在

TE Connectivity

Lead Data Scientist

6月 2024 - 現在

Technical Writer and Researcher

IGI Cybersecurity Group

2月 2021 - 現在

Senior Data Scientist

Ernst and Young

9月 2021 - 6月 2024

Data Scientist ( Associate)

RX91 Web Private Limited

12月 2020 - 9月 2021

Scientific Writer and Editor

Datatron.io

4月 2021 - 6月 2021

Assistant Data Analyst

Medtoureasy

9月 2020 - 11月 2020

学歴

MSc Data Science and Machine Learning

University of Missouri - Rolla

12月 2019 - 7月 2021

認定資格

• POWER BI MASTER CLASS - DASHBOARDS AND POWER BI SERVICE 2022

  udemy

  6月 2022 - 現在

• SPARK AND PYTHON FOR BIG DATA WITH PYSPARK

  udemy

  3月 2022 - 現在

• BUSINESS ANALYSIS AND PROJECT MANAGEMENT

  coursera

  10月 2021 - 現在

• AWS Cloud Practitioner Essentials

  AWS

  5月 2021 - 現在

• Machine Learning

  NPTEL

  6月 2020 - 現在

• Data Science for Engineers

  NPTEL

  2月 2020 - 現在

• Python

  Hacckerank

  2月 2020 - 現在