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 - 10+ 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.