Key Responsibilities

1. Leadership and 0-to-1 Strategy
2. Team Building: Recruit, mentor, and manage a hybrid team of data engineers, data scientists, and ML engineers from scratch
3. Unified Roadmap: Define and execute a multi-year technical vision that bridges scalable data infrastructure with advanced AI capabilities
4. 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)
5. Data Engineering & Infrastructure
6. 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
7. 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)
8. Data Pipelines: Build automated, resilient ETL/ELT workflows using tools like Apache Airflow, dbt, and PySpark to ensure high data quality and governance
9. Artificial Intelligence & Machine Learning
10. 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
11. Rider Intelligence: Build algorithms to analyze rider behavior, detect anomalies (e.g., accident or fall detection), and personalize the app/scooter experience
12. 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

1. 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
2. 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
3. Languages: Expert-level proficiency in Python and SQL
4. Data Engineering: Deep practical experience with stream processing (Kafka, Spark Streaming, Flink), cloud data warehouses, and orchestration (Airflow, dbt)
5. AI/ML Frameworks: Strong hands-on experience with machine learning libraries (Scikit-learn, XGBoost, Pandas) and deep learning frameworks (PyTorch or TensorFlow)
6. MLOps: Proven track record of deploying machine learning models into live production environments and monitoring model drift
7. 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
8. Mandatory 5 days work from office