About the role
We are seeking a seasoned Senior Data Scientist with 8-10 years of experience to join our innovative team at Magenta Mobility. You will play a critical role in analyzing and leveraging data to enhance cross-utilization of our EV fleet, maximize yield per deployed asset, and develop strategies for seamless operations. The ideal candidate is a strategic thinker with deep technical expertise, exceptional problem- solving skills, and a passion for using data to track vehicle performance, prevent breakdowns, and drive efficiency in the EV logistics Design and develop AI-driven solutions for EV fleet cross-utilization challenges, including optimizing vehicle sharing across routes, clients, and time slots to maximize yield per asset.
- Handle and analyze data related to cross-utilization of vehicles, identifying patterns in usage,downtime, and efficiency to inform asset deployment strategies.
- Advise and lead prioritization of research areas supporting data management, governance, and optimization of EV fleet performance, with a focus on reducing breakdowns through predictive optimization of EV fleet performance, with a focus on reducing breakdowns through predictive analytics.
- Collaborate with business and corporate functions to identify and co-develop solutions focused on logistics challenges, such as demand forecasting, route optimization, and strategies to ensure smooth operations across the fleet.
- Analyze large datasets to uncover trends, patterns, and insights that improve EV fleet efficiency, increase yield per deployed asset, track vehicle breakdowns, and propose actionable strategies for preventive maintenance and operational improvements.
- Build and maintain critical data pipelines and architectures across technical areas to support Magenta Mobility's business objectives in EV logistics and asset management.
- Communicate complex findings and recommendations such as cross-utilization metrics and breakdown risk assessments to technical and non-technical stakeholders through compelling data visualizations and reports.
- Mentor junior data scientists and contribute to fostering a data-driven culture focused on sustainable transportation and fleet optimization.
Location & commitments
Employment type : Full-time, PermanentLocation : Navi MumbaiWorking days : Monday to Friday + alternate SaturdaysCandidate requirements
Experience & Qualifications Required
Master's Degree from B school.8-10 years of professional experience in data science or a related role, preferably in logistics, transportation, or fleet management.Proven experience in deploying and managing machine learning models in production environments, particularly for real-time fleet data and asset utilization.Strong ability to monitor ML models in production, addressing model performance, data quality issues, and anomalies related to vehicle breakdowns or utilization inefficiencies.Working knowledge of security best practices and compliance standards for Machine Learning systems in operational contexts.Experience with infrastructure optimization techniques to enhance performance and efficiency in logistics and fleet data systems.Development of REST APIs using frameworks such as Flask or Fast API for seamless integration into business solutions, including fleet tracking dashboards.Required Technical Skills
Machine Learning & AI : Proven experience with a wide range of machine learning algorithms and deep learning techniques, applied to predictive maintenance and asset optimization.Programming Languages : Expertise in Python.Data Science Libraries : Hands-on experience with Pandas, Scikit-learn, and deep learning frameworks like TensorFlow.Cloud Platforms : Practical knowledge of at least one major cloud provider (Azure, AWS, GCP) including their data science and machine learning services for handling fleet telemetry data.Database & Big Data : Familiarity with SQL and big data technologies (e.g., Spark, Databricks) for processing large-scale EV utilization datasets. processing large-scale EV utilization datasets.Data Analysis & Visualization : Conduct in-depth data analysis on cross-utilization metrics, providing actionable insights on yield per asset, breakdown trends, and creating compelling providing actionable insights on yield per asset, breakdown trends, and creating compelling data visualizations to communicate findings to stakeholders.(ref : iimjobs.com)