Vivek Singhal, PhD.

AI Researcher

Mohali, India5 yrs 3 mos experience

Highly Stable

Key Highlights

Led AI initiatives improving energy forecasting accuracy.
Ph.D. holder with full-stack data science expertise.
Passionate about AI-driven digital transformation.

Stackforce AI infers this person is a Data Scientist specializing in AI and Machine Learning for the Energy sector.

Contact

Skills

Core Skills

Machine LearningDeep LearningPredictive Modeling

Other Skills

Time Series AnalysisRetrieval-Augmented Generation (RAG)PythonSQLPostgreSQLMongoDBPandas (Software)StatisticsData VisualizationTensorFlowTableauData ScienceData AnalysisStatistical ModelingNatural Language Processing (NLP)

About

Senior Lead Data Scientist with 5+ years of experience leading impactful AI initiatives across Machine Learning, Deep Learning, Time Series Forecasting, Predictive Modeling, and GenAI I specialize in designing and deploying scalable AI solutions that deliver measurable business outcomes—particularly in the energy and analytics domain. I bring full-stack expertise across the data science lifecycle—from data engineering and feature design to model development, optimization, and production deployment. Proficient in Python, SQL, PostgreSQL, MongoDB, RAG and Flask APIs, I lead cross-functional teams to build forecasting and analytics platforms that enhance decision intelligence, operational reliability, and strategic performance. Holding a Ph.D. from IIT Roorkee, I combine research-driven insight with practical implementation—leveraging statistical modeling, deep learning architectures (CNN, LSTM), algorithm optimization, and applied AI techniques to solve complex, real-world challenges. Currently focused on advancing Generative AI and LLM-driven forecasting systems, I aim to integrate AI innovation into core business processes, enabling smarter, data-driven, and energy-efficient decision systems. I’m passionate about using AI as a strategic catalyst for digital transformation, driving operational excellence, and building sustainable, future-ready intelligence solutions.

Experience

5 yrs 3 mos

Total Experience

5 yrs 3 mos

Average Tenure

5 yrs 3 mos

Current Experience

Oati

3 roles

Senior Lead Data Scientist

Promoted

Dec 2024 – Present · 1 yr 5 mos · Hybrid

● Leading the development of AI-driven forecasting models for enhanced predictive analytics in energy systems.
● Driving innovation in time-series modeling by optimizing load forecasting models with deep learning techniques.
● Spearheading data-driven solutions to improve energy grid efficiency, reducing forecast errors by 10-15%.
● Managing a team of data scientists and analysts, ensuring high-quality model deployment and continuous improvements.
● Collaborating with cross-functional teams to integrate advanced analytics into business decision-making processes.

Time Series AnalysisRetrieval-Augmented Generation (RAG)Deep LearningPredictive ModelingMachine LearningPython+3

Senior Data Scientist

Dec 2022 – Nov 2024 · 1 yr 11 mos · Hybrid

● Developed a machine learning system with 90% accuracy, improving data processing efficiency by 25% and ensuring compliance by distinguishing residential from non-residential electric vehicles in a 15% annual growth market.
● Built real-time and day-ahead forecasting models with ~90% accuracy, improving price prediction reliability by 20%.
● Designed an ensemble model with 98% accuracy, enhancing future load forecasting accuracy by 30%.
● Created a time-series algorithm that reduced missing data errors by 90-95%, improving forecast reliability by 40%.
● Led optimization efforts, improving predictive performance by 35% through hyper parameter tuning and advanced feature engineering.
● Conducted rigorous validation against real-world data, increasing forecasting accuracy by 15%.

Pandas (Software)MongoDBStatisticsData VisualizationTensorFlowTableau+9

Data Scientist

Dec 2020 – Nov 2022 · 1 yr 11 mos · Hybrid

● Developed a forecasting model for load and solar generation, achieving 90% accuracy and reducing forecasting errors by 15%.
● Implemented a deep learning model for load forecasting, attaining 95% accuracy, which led to a 25% improvement in prediction quality.
● Increased data accuracy by 5-7% through targeted resolution of data issues, contributing to a 20% reduction in data-related errors.
● Optimized deep learning models by fine-tuning hyperparameters with grid search methods in Python, resulting in a 10% improvement in overall forecasting performance and a 30% reduction in model training time.

Pandas (Software)MongoDBStatisticsData VisualizationTensorFlowTableau+9