Data Science & Analytics Lead
Debanjan Chakraborty supports Vector Labs’ data intelligence direction through applied data science, analytics engineering, dashboarding, time-series anomaly detection, ETL workflows, and machine learning systems. His background connects industrial data, business reporting, and applied AI into systems that help organizations understand what is happening and decide what to do next.
Focus
Debanjan’s work focuses on transforming raw, scattered, or operational data into structured workflows, dashboards, and analytical systems. His strength sits between data engineering, business analytics, and applied machine learning - the layer where data becomes useful for teams, stakeholders, and decision-makers.
Experience Highlights
Worked as a Working Student Data Scientist at Siemens Energy, developing a Python microservice to retrieve events from an external API and publish them to Azure Event Hub as part of a proof of concept for Multi-Agent System integration with the EcoMAIN platform.
Impact: Strengthens Vector Labs’ ability to design data services that connect industrial systems, event streams, and intelligent automation workflows.
Worked with MS SQL databases, SharePoint, Excel, Power Automate, Alteryx, and Tableau to migrate, synchronize, process, and visualize portfolio and development data.
Impact: Brings practical experience in turning fragmented business data into structured databases, analytical dashboards, and stakeholder-facing reports.
Built Power BI dashboards for finance, sales, marketing, supply chain, and executive reporting using MySQL, Excel, Power Query, and transformed datasets.
Impact: Helps Vector Labs build reporting systems that make business performance, operations, and strategic indicators easier to understand.
Master’s thesis focused on an anomaly detection agent for time-series data in the process industry, designed for integration into a Multi-Agent System under Siemens Energy.
Impact: Adds domain strength in industrial anomaly detection, time-series intelligence, and agent-oriented analytical systems.
Selected Projects
Assistive Navigation / Computer Vision
Designed an assistive navigation system combining DeepLabv3+ ResNet50 semantic segmentation with Valhalla GPS routing to support real-time sidewalk guidance.
Impact: Demonstrates the use of computer vision and routing intelligence to make navigation more accessible and context-aware.
AI-Powered Multilingual Chatbot
Architected a full-stack German driving-law chatbot using React/TypeScript, FastAPI, LangChain RAG, Chroma, Perplexity, Serper fallback, Firebase authentication, and Dockerized deployment.
Impact: Shows practical experience in building language-model systems that combine retrieval, multilingual interaction, and production deployment.
ETL / Urban Analytics
Built an end-to-end ETL and analysis pipeline correlating Chicago crime incidents with weather data using Python, SQLite, exploratory analysis, statistical modeling, visualizations, and CI/CD workflows.
Impact: Demonstrates how multi-source data can be structured into insight for urban analysis, correlation discovery, and reproducible data workflows.
Technical Stack
What Debanjan adds to Vector Labs
Helps structure messy operational data into pipelines, databases, and usable analytical foundations.
Builds reporting layers that turn processed data into clear visual insight for teams and leadership.
Connects ML, anomaly detection, and analytical modeling to real business and industrial use cases.
From dashboards and ETL workflows to time-series anomaly detection and applied machine learning, Vector Labs helps organizations turn data into direction.