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Data Scientist with experience in Data Engineering, Full Stack Development,
and AI/ML projects. I enjoy creating impactful solutions, collaborating with teams,
and turning ideas into real-world products.
I value clean code, scalable architectures, and intuitive user experiences.
Hi! My name is Manav. I'm a Data Scientist with previous Software Engineering experience and an AI enthusiast passionate about building things that live on the internet and solve real-world problems. My journey into tech began in college when I discovered a love for creating software beyond just assignments.
Since then, I’ve built AI agents, full-stack applications, Deep Learning Projects, and competed in multiple hackathons, deepening my passion for development and problem-solving. I enjoy coding from scratch and turning new ideas into working products.
Outside of tech, I love playing cricket, working out, watching anime, and listening to music — always finding inspiration beyond the screen.
PYTHON
JAVA
SCALA
GOLANG
C
C++
HTML
CSS
JS
BOOTSTRAP
FLASK
DJANGO
ANGULAR
SPRING BOOT
REACTJS
NODEJS
PYTORCH
TENSORFLOW
KERAS
OPENCV
HADOOP
DOCKER
GITHUB
GIT
JIRA
LINUX
AIRFLOW
DB2
MYSQL
SNOWFLAKE
MONGO DB
POSTGRESQL
• Coursework – Machine Learning Algorithms, Natural language Processing, Design and Analysis of Algorithms, Networked Systems, Probabilistic Models, AI Agents, Recommenders System, Big Data & Knowledge, Machine Learning for Music
At Dell Technologies, I’m working on optimizing North America fulfillment operations. My work involves engaging with business and operations teams to improve throughput forecasting, bulk window consolidation, and digitized planning — enabling projected multi-million dollar run-rate and EBIT benefits. I’ve developed an end-to-end storage utilization model that maps parts across racks, pallets, fulfillment centers, and geographies, implementing optimization logic to mitigate overcapacity risks during fluctuating inbound and outbound demand. I also build scalable data pipelines and workflows to support advanced modeling, exploring time-series forecasting and clustering techniques to improve accuracy and enable proactive allocation decisions during peak cycles.
At Morgan Stanley, I worked on modernizing critical systems by migrating legacy
microservices to Azure, containerizing deployments with Docker, and optimizing
performance through multithreading. I contributed to building a real-time credit
risk calculator that helped drive $230M in revenue, automated regression workflows
to save analyst time, and developed anomaly detection systems to improve production
reliability. I also wrote comprehensive unit and BDD tests, enhanced CI/CD pipelines,
and mentored an intern to automate internal documentation workflows, boosting team efficiency.
Recognized with “Debutant of the Year” award for impactful contributions in automation and credit risk analytics
During my internship, I designed and built a JVM-based data integration framework using Java and Scala-Spark, improving data reliability and cutting processing times. I also optimized Spring Boot services for high-frequency workloads, significantly reducing data handling latency.
• Coursework – Software Engineering, Data Structures, Algorithms, Database Management, Object Oriented Programming, Big Data & Analysis, Operating System • Assisted 70+ students in programming through tutorials, grading, and mentoring, providing detailed solutions and improving learning outcomes. • Finalist at JP Morgan Chase’s Code for Good (2019 & 2020), building software solutions for non-profits. • Won the Best Project and Best Research Paper awards in our department, along with the Best Innovative Project award from an education startup ”Aas Vidhyalaya” from over 100+ projects. • Mentor, Computer Society of India (CSI) – SPIT Hackathon (Feb 2021); guided 12 teams with insights and feedback on projects
PokéBattleBots: LLM Agents for Pokémon Showdown - Developed a modular system where large language model (LLM) agents play Pokémon battles by analyzing game states, making decisions using reinforcement learning, and adapting strategies through prompt engineering.
VisionScribe: AI Image Caption Generator - Created an AI model that looks at images and automatically generates human-like captions using a deep learning pipeline combining CNNs and LSTMs with smart decoding techniques.
AirMouse: Gesture Controlled Virtual Mouse - Designed a virtual mouse that lets users control their computers using hand gestures, using computer vision techniques like OpenCV and CNNs for accurate gesture tracking.
BlockVote: Secure Blockchain-Based E-Voting - Built a permissioned blockchain-based electronic voting platform that ensures secure, transparent, and tamper-proof elections using Hyperledger Fabric and cryptographic authentication.
Built a web platform that verifies user identities by scanning documents with OCR, performing live face recognition, and adding security layers like IP tracking to prevent fraud.
EcoNet: Energy Efficient Wireless Sensor Networks - Developed an optimized sleep scheduling system for wireless sensor networks using Particle Swarm Optimization (PSO), reducing power consumption by identifying redundant nodes and putting them into hibernation without compromising network coverage.
