Nayan R

About Nayan R

Hello I\'m Nayan Dilipkumar Rawwal, a data-driven professional from Mumbai with an academic foundation in M.Sc. in Data Science and B.Sc. in Information Technology. Since 2021, I\'ve been applying my skills across various domains within Tata Consultancy Services (TCS) — evolving from data validation and quality engineering to taking on impactful roles in Data Analysis and Data Science. My current work spans across logistics analytics, forecasting, recommendation systems, and business intelligence — where I solve real-world problems using Python, SQL, Power BI, and ML models. I’ve worked on:a)Trade-lane volume & profit forecastingb)Door Customer recommendation & retention modelingc)ZIP-to-CFS distance computation and optimization (150k+ ZIPs)d)Power BI dashboards for customer behavior and attrition analysise)Scalable geolocation pipelines and correction factor modelingf)Reporting, AutomationI enjoy building data products that drive decisions, and my passion lies in bridging the gap between raw data and strategic business value. I\'m curious, growth-oriented, and always ready to embrace complex challenges with a problem-solving mindset.#KeepLearning is my daily motto — whether it\'s mastering a new library, streamlining a pipeline, or delivering clearer insights to stakeholders. Tools & Technologies:1.Power BI, Advanced Excel2.Python, Jupyter Notebook3.SQL, MySQL4.Pandas, scikit-learn5.OpenRouteService, geopy, pgeocode, etc6.Veeva Vault, DMW7.Streamlit Feel free to connect or reach out at nayanrawal@b•••@gmail.com — always open to collaborating, sharing, and learning.

Experience

1. Data Scientist

   Allcargo Global

   Jan 2025 — Present · Maharashtra, IN

   1.Built and deployed machine learning models to forecast monthly trade-lane volume and gross profit using time-series features and rolling metrics.2.Designed a customer retention classification model based on drop in pickup/delivery share, leveraging lag-based features and business rules.3.Engineered features volume trends, growth rates, lag decay, customer activity windows, and holiday-adjusted working day counts.4.Created scalable pipelines for training, testing, and predicting on unseen data while ensuring target leakage prevention and temporal consistency.5.Collaborated closely with business stakeholders to translate domain logic into measurable model targets and KPIs for ongoing model validation.

Education

• University of Mumbai

  Bachelor's degree, Information Technology

  2017 — 2020

• University of Mumbai

  Master's degree, Data science