About River
River is a design company building multi-utility products. Our flagship product River Indie #SUVofScooters, is designed to help you get things done. Engineered to be a dependable ally on your road to success.
We're a 1000+ team headquartered in Bengaluru – backed by marquee international investors, with mobility-focused funds linked to Yamaha Motors, Al-Futtaim Automotive Group, Toyota VC, Trucks Venture Capital, Mitsui & Co. Ltd, Marubeni Ventures Inc., Lowercarbon Capital, and Maniv Mobility.
Key Responsibilities
- Leadership and 0-to-1 Strategy
- Team Building: Recruit, mentor, and manage a hybrid team of data engineers, data scientists, and ML engineers from scratch
- Unified Roadmap: Define and execute a multi-year technical vision that bridges scalable data infrastructure with advanced AI capabilities
- Cross-Functional Impact: Partner with Vehicle Engineering, Software, Manufacturing, and Sales to identify high-impact AI/Data use cases (e.g., supply chain forecasting, smart scooter features)
- Data Engineering & Infrastructure
- IoT Telemetry Pipelines: Architect low-latency, high-throughput streaming pipelines to ingest real-time data from vehicle sensors (VCU, BMS), mobile apps, and charging infrastructure using MQTT, Apache Kafka, or AWS Kinesis
- Modern Data Stack: Design and scale a unified Lakehouse/Warehouse (e.g., Databricks, Snowflake) to handle both streaming telemetry and complex enterprise data (ERP, CRM, MES)
- Data Pipelines: Build automated, resilient ETL/ELT workflows using tools like Apache Airflow, dbt, and PySpark to ensure high data quality and governance
- Artificial Intelligence & Machine Learning
- Predictive Modeling: Develop and deploy ML models specific to the EV ecosystem—such as predictive maintenance, Battery State of Health (SoH) degradation forecasting, and dynamic Range/State of Charge (SoC) estimation
- Rider Intelligence: Build algorithms to analyze rider behavior, detect anomalies (e.g., accident or fall detection), and personalize the app/scooter experience
- MLOps & Deployment: Establish the MLOps infrastructure (e.g., MLflow, Kubeflow) to train, deploy, monitor, and retrain models seamlessly in production—both in the cloud and on edge devices (vehicle ECUs)
Ideal Candidate
- Experience: 8–12+ years of comprehensive experience across Data Engineering and Data Science, with at least 2–3 years leading technical teams or complex, multi-disciplinary data projects
- Domain Expertise: Prior experience in Automotive, EV, Telematics, or IoT is highly preferred. You must be comfortable dealing with high-frequency time-series data and geospatial (GPS) data
- Languages: Expert-level proficiency in Python and SQL
- Data Engineering: Deep practical experience with stream processing (Kafka, Spark Streaming, Flink), cloud data warehouses, and orchestration (Airflow, dbt)
- AI/ML Frameworks: Strong hands-on experience with machine learning libraries (Scikit-learn, XGBoost, Pandas) and deep learning frameworks (PyTorch or TensorFlow)
- MLOps: Proven track record of deploying machine learning models into live production environments and monitoring model drift
- Player-Coach Mentality: You possess the strategic vision to design enterprise architecture, but you still love writing production-grade code, debugging PySpark jobs, and tuning neural networks
- Mandatory 5 days work from office
