Overview

Location: Bengaluru, Karnataka

Responsibilities

• Support the team in day-to-day data and machine learning tasks.
• Help with data collection, cleaning, preprocessing, and exploratory analysis.
• Assist in running experiments and evaluating different modelling approaches under guidance.
• Work with senior engineers to understand how ML systems are built, tested, and deployed.
• Document your work, experiments, and findings clearly for the team.
• Contribute to internal tooling, scripts, and reproducible workflows.
• Share insights from data and present results in team discussions.
• Continuously learn — through reading, experimenting, and collaborating with the team.
• Build and deploy end-to-end ML and forecasting systems.
• Develop intelligent data pipelines for predictive and probabilistic analytics.
• Work on real-world time series problems in energy, infrastructure, and sustainability domains.
• Collaborate with engineering teams to integrate ML models into scalable systems.
• Monitor and improve model performance, accuracy, and reliability.
• Contribute to building AI-driven decision-making platforms.

Qualifications

• Working knowledge of ML or optimization concepts, with practical exposure through projects or coursework.
• Strong mathematical and statistical foundation.
• Passion for scalable systems, automation, and applied problem-solving.
• Startup DNA → bias to action, comfort with ambiguity, love for fast iteration, and flexible and growth mindset.
• Shape a first-of-its-kind AI + clean energy platform.
• Work with a small, mission-driven team obsessed with impact.
• An aggressive growth path.
• A chance to leave your mark at the intersection of AI and sustainability.

Education

• Currently pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Engineering, Applied Mathematics, Statistics, Data Science, or a related quantitative field.
• 0–1 year of experience (academic projects, coursework, hackathons, or open-source contributions are welcome).
• A solid foundation in mathematics and statistics.
• Genuine interest in machine learning and applied AI.
• Comfortable programming in Python and working with libraries like NumPy and Pandas.
• Familiarity with basic machine learning concepts (e.g., regression, classification, clustering).
• Awareness of how data is structured and analysed (Jupyter Notebooks, basic visualisation).
• Familiarity with Git/GitHub for version control.
• Strong problem-solving skills and attention to detail.
• Good written and verbal communication.
• A learning mindset — willingness to ask questions, take feedback, and pick up new tools quickly.