Job Description

Key Role & Responsibilities:

The Data Scientist shall be responsible for the following:

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  Work closely with the data team on the design and build of data pipelines and backend services powering data ingestion, processing, and access across the platform.
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  Develop and maintain data models and ML models, ensuring they are production-ready and aligned with platform use cases.
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  Assess available data to form hypotheses on how data can be used for data science and ML use cases, informing the data collection and architecture approach.
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  Support hypothesis testing across candidate use cases to identify which have potential for further development.
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  Work with the data team on the analytics layer to ensure data models and outputs are accessible and meaningful to downstream consumers.
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  Produce documentation covering model development decisions and pipeline design.
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  Work closely with internal team members to progressively transfer data science and ML modelling capability.

Requirements:

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  Degree in Computer Science, Engineering, Data Science, or related field with 8+ years of experience in data science.
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  Proven experience developing and deploying data models and ML models in production environments at scale.
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  Ability to work collaboratively with data engineers on pipeline design, backend services, and analytics layer development.
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  Experience with modern data platform tooling (e.g. Databricks or equivalent), delivered on AWS or comparable cloud infrastructure.
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  Proven track record leading data science delivery for data platforms at national or large and complex scale, having served as the lead data scientist for the majority of the project's duration, including demonstrated experience delivering comparable projects internationally at city-level or above, beyond the Singapore market.
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  Familiarity with standard IoT and industrial messaging protocols (e.g. MQTT, AMQP, OPC-UA) for ingesting data from sensors, robotics, and industrial equipment.
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  Experience with robotics data formats and the ingestion and processing of sensor or field-equipment data streams is preferred.
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  Experience preparing data infrastructure for GenAI use cases, including building pipelines that support large language model or retrieval-augmented generation workloads, is preferred.
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  Experience building ingestion pipelines for robotics or field-sensor data, including familiarity with robotics telemetry, is preferred.