Data Scientist (Machine Learning / Data Model)
Summary
Build and deploy production-scale ML models and data pipelines, collaborating with engineers to process industrial IoT data and explore GenAI use cases.
Responsibilities
- Design and develop scalable data pipelines and backend services for data ingestion and processing.
- Build, deploy, and maintain production-ready data science and machine learning models.
- Analyse data to identify opportunities for AI/ML use cases and validate hypotheses.
- Collaborate with data engineers to develop data models and analytics solutions.
- Document model development, data pipelines, and technical decisions.
- Support knowledge transfer and capability building within the team.
Requirements
- Degree in Computer Science, Data Science, Engineering, or a related discipline.
- 8+ years of experience in data science, with a strong track record of delivering production-scale ML solutions.
- Hands-on experience with machine learning model development, deployment, and data engineering collaboration.
- Experience with modern data platforms (e.g. Databricks) and cloud environments such as AWS.
- Proven experience leading data science initiatives for large-scale, complex data platforms.
- Knowledge of IoT and industrial messaging protocols (e.g. MQTT, AMQP, OPC-UA).
- Experience with robotics, sensor, or industrial data processing is an advantage.
- Exposure to GenAI, LLM, or Retrieval-Augmented Generation (RAG) data pipelines is preferred.