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Neenad Parte
Data Scientist III, AI and Analytics
BaCurrently at Barnes & Noble
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About
I am a Data Scientist III at Barnes & Noble, on the AI and Analytics team, where I build student enrollment prediction models that drive textbook inventory decisions across more than 700 universities. Before this I spent close to three years at Accenture in Mumbai on demand forecasting, and built a retrieval augmented GenAI chatbot over internal demand databases. Day to day that means Python, LightGBM and XGBoost, time series forecasting with ARIMA, SARIMAX and Prophet, and GenAI work with GPT-4, LangChain and vector stores. M.Sc in Statistics and Data Science from NMIMS, B.Sc in Statistics from K.J. Somaiya. Book me for mock interviews on data science and machine learning, for career clarity if you are weighing analyst against scientist against ML engineer, or a services firm against a product company, and to reframe a data resume that is not getting callbacks. I moved roles recently, so the interview loop is fresh, and I have mentored junior data scientists from the other side of it.
How I can help
Jobmonger Career Session. 60-minute live session, Pre-session questionnaire, Practical post-session deliverable, Quality check before delivery, 48-hour touchback.
Experience
Ba
Data Scientist III, AI and Analytics
Barnes & NobleCurrent
Apr 2026 to present- Delivers weekly student enrollment predictions for First Day Complete across more than 700 universities, using LightGBM
- Raised prediction accuracy from 80 to 82 percent by engineering a feature for the week on week swings in estimated enrollment
- Out of time backtesting on prior terms showed a 7 to 8 percent reduction in textbook overstock
- Added feature weighting and historical trend based null imputation for a further gain of about 1 percent
- Cut execution time by up to 1.5 hours per run through automation scripting
- Started bias detection and correction work to improve model fairness and prediction reliability
Ac
Data Scientist
Accenture
Aug 2023 to Apr 2026- Quarterly demand forecasts across 50 plus product categories, supporting more than 20 million dollars in inventory and merchandising decisions
- Lifted brand level forecast accuracy from 78 to 80 percent, and built NNAR and UCM models for low volume SKUs for a further 2 to 4 percent monthly gain
- Built a RAG chatbot over internal demand databases with GPT-3.5 and GPT-4, LangChain and FAISS, cutting forecast analysis time by 40 percent
- Automated forecasting pipelines and BAU workflows, saving 20 to 24 hours per cycle
- Co-built a Streamlit retail sales forecaster with multi model forecasting and scenario simulation, adopted by non technical stakeholders
- Presented forecast insights to senior managers, associate directors and MDs, and mentored junior data scientists
Ae
Data Science Intern
Aeries Technology
Feb 2023 to Jul 2023- Linear regression financial forecasting models, improving cost projection accuracy by 4 percent
- Built a RAG chatbot with FALCON-7B, LangChain and Chainlit over internal knowledge bases
Vi
Data Science Intern
Vista Intelligence
May 2022 to Jul 2022- Analysed more than 100,000 Twitter posts for commodity and forex market sentiment
- DistilBERT sentiment classification on USD/INR and gold market data at over 80 percent accuracy
Highlights and awards
Star of Business Award· Accenture, 2024Core Value Champion Award· Accenture, 2024M.Sc. Statistics and Data Science· NMIMS University, Mumbai, 2023B.Sc. Statistics· K.J. Somaiya College of Science and Commerce, Mumbai, 2021Google Data Analytics Professional Certificate· CourseraDatabricks Fundamentals Accreditation· Databricks
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