Data Scientist

Role Overview

This position involves collaborating closely with the data team to architect and implement data pipelines and backend services that facilitate data ingestion, processing, and availability across the platform. The candidate will be responsible for developing scalable data models and machine learning solutions, ensuring their readiness for production and alignment with the platform's intended use cases.

Key Responsibilities

• Partner with the data team to design and construct data pipelines and backend services powering platform data flows.
• Create and sustain data and machine learning models optimized for production environments.
• Analyze available datasets to hypothesize potential data science and machine learning applications, shaping data collection and architecture strategies.
• Collaborate with internal teams to incrementally transfer expertise in data science and machine learning modeling capabilities.
• Conduct hypothesis testing to determine promising use cases warranting extended development.
• Work alongside the data team to ensure analytics outputs and data models are accessible and interpretable by downstream users.
• Generate comprehensive documentation detailing model development choices and pipeline architecture.

Candidate Profile & Requirements

• A degree in Computer Science, Engineering, Data Science, or a related discipline with over 8 years of experience in data science roles.
• Demonstrated ability to develop and deploy data and machine learning models at scale within production environments.
• Strong collaboration skills with data engineers regarding pipeline design, backend service development, and analytics layer implementation.
• Experience working with contemporary data platform technologies, such as Databricks or similar tools, preferably deployed on AWS or equivalent cloud platforms.
• Proven leadership in delivering data science projects for large-scale or national data platforms, predominantly as the lead data scientist, including international or city-level deployments beyond Singapore.
• Familiarity with standard IoT and industrial messaging protocols like MQTT, AMQP, and OPC-UA for sensor and industrial equipment data integration.
• Preferred experience with robotics data formats and processing sensor or field equipment data streams.
• Preferred expertise in building data ingestion pipelines for robotics or field sensors, especially those involving robotics telemetry.
• Preferred skills in setting up data infrastructure supporting Generative AI use cases, including pipelines for large language models or retrieval-augmented generation tasks.