Tanush G.

I am a results-oriented data scientist with a strong foundation in computer science and engineering, currently working at Livguard Drivetrain, a leading e-mobility solutions provider. On a given work day I specialize in extracting actionable insights from IoT data generated by battery-swapping stations and batteries, enabling data-driven decision-making for the business. With experience in cleaning, analyzing, and visualizing large datasets, I’ve successfully influenced strategic initiatives, including identifying at-risk battery assets and reducing unethical subscription sharing, which led to a 15% decrease in invalid battery swaps, or clustering station based on a custom health metric to reduce operational TAT. I thrive in fast-paced, high-impact startup environments, where I work to align data insights with the company’s medium- and long-term vision. As the sole data scientist on my team, I independently handle data science and analytics responsibilities, directly contributing to the R&D team’s objectives. Skills and Expertise: • Data Analysis and Visualization: Proficient in using Python and libraries like Pandas, NumPy, Matplotlib, and Seaborn for analyzing and presenting complex datasets understandably. • Predictive Modeling: Skilled in building and validating machine learning models using Scikit-learn to solve business challenges and predict key metrics. • IoT Data Expertise: Experienced in working with IoT data from distributed systems like battery-swapping stations to uncover patterns, optimize processes, and improve operational efficiency. • Problem Solving: Adept at identifying and solving problems, such as minimizing operational inefficiencies and detecting unethical behavior, with direct business impact. • Collaboration and Communication: Strong ability to collaborate with stakeholders across teams, presenting insights to technical and non-technical audiences to guide decision-making. • Startup Environment Adaptability: Comfortable working independently and taking ownership of projects in dynamic, high-pressure environments. Technical Skills: • Programming: Python (with libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn) • Data Wrangling: Cleaning and preparing raw datasets for analysis • Data Visualization: Creating meaningful visualizations to communicate data insights effectively • Tools: Jupyter Notebook, Git/GitHub, Excel • Concepts: Machine learning, predictive analytics, statistical analysis Professional Achievements: • Reduced invalid battery swaps by 15% by identifying and addressing unethical subscription sharing. • Designed models to predict at-risk battery assets, improving operational efficiency and reducing downtime. • Delivered impactful insights to senior leadership, directly contributing to the company’s medium- and long-term strategic goals.