Jatin Jain

Software Development Engineer | Data Scientist
Dhanbad, IN.

About

Highly analytical Software Development Engineer with a strong foundation in data science and full-stack development. Leverages expertise in predictive analytics, real-time systems, and robust front-end architecture to drive quantifiable business outcomes. Proven ability to optimize complex processes and deliver high-impact technical solutions across diverse industries, from financial markets to e-commerce and SaaS platforms.

Work

SOTAOG
|

SDE

Summary

Developed and maintained Angular modules for a SaaS platform, enhancing predictive analytics to optimize oil and gas production, supply-demand, and cash flow.

Highlights

Built and maintained Angular modules for a SaaS platform, delivering predictive analytics to optimize oil and gas production, supply-demand, and cash flow.

Collaborated with cross-functional teams, including data scientists and backend engineers, to implement user-facing features and improve UI performance and responsiveness.

Contributed to scalable front-end architecture, integrating complex data visualizations for seamless interaction with predictive analytics models and real-time operational insights.

Futures First
|

Market Analyst

Summary

Actively traded U.S. commodities on the CME exchange, leveraging in-depth market analysis and strategic risk management to drive profitable trading decisions.

Highlights

Actively traded U.S. commodities on the CME exchange, managing positions and executing real-time trades to capitalize on market opportunities.

Conducted in-depth market analysis, utilizing technical and fundamental strategies to drive informed trading decisions and optimize returns.

Monitored global commodity markets, economic trends, and news to identify and exploit profitable trading opportunities, informing strategic moves.

Managed risk and capital allocation effectively to ensure consistent performance and minimize potential losses across all trading activities.

1 Click Tech
|

SDE

Summary

Led front-end development for an online auction platform, implementing real-time bidding and robust security measures to ensure seamless and secure user transactions.

Highlights

Spearheaded the development of an online time-based auction platform for heavy machinery using Next.js and TypeScript, ensuring robust and efficient front-end performance.

Implemented real-time bidding functionality, enabling seamless user participation in auctions and facilitating instantaneous transaction processing.

Created a comprehensive bid management system to track, display, and manage user bids in real-time, enhancing operational oversight and responsiveness.

Integrated stringent security measures to ensure the integrity and confidentiality of auction data and transactions, safeguarding user trust.

Collaborated with cross-functional teams to gather requirements, aligning development efforts with key business goals and ensuring successful feature delivery.

Education

Indian Institute of Technology, Dhanbad
Dhanbad, Jharkhand, India

Bachelor of Technology

Electronics and Communication Engineering

Courses

Data Structures and Algorithms

Statistics & Probability

Applied Machine Learning & Data Science

Skills

Programming Languages

C++, JavaScript, Python, TypeScript, C, SQL.

Libraries & Frameworks

Pandas, Scikit-learn, Matplotlib, PyTorch, TensorFlow, Statsmodels, ReactJS, NextJs, Angular.

Data Science & Machine Learning

Applied Machine Learning & Data Science, Statistics & Probability, Time Series Forecasting, Feature Engineering, Model Evaluation, Predictive Analytics, LightGBM, SARIMAX, WRMSSE, SMAPE.

Software Development & Architecture

Data Structures and Algorithms, Front-End Development, Real-time Systems, Scalable Architecture, UI Performance, Bid Management Systems, Auction Platforms, Cross-functional Collaboration.

Competitive Programming

Codeforces (Expert, 300+ problems), Leetcode (275+ problems), HackerRank (6 star, 100+ problems).

Projects

M5 Forecasting (Walmart – WRMSSE)

Summary

Designed and implemented a multi-model forecasting pipeline to predict 28-day sales for 3,000+ products across 10 stores, optimizing inventory management and achieving a 0.12 WRMSSE.

Store Item Demand Forecasting (SMAPE)

Summary

Developed and evaluated time series forecasting models for daily sales across 50 store-item combinations, achieving a competitive SMAPE of 11.4%.