Data Scientist – Process Control

About STRELI

STRELI Technology Limited is a growing technology startup working at the intersection of industrial intelligence, smart manufacturing, and automation. We develop industrial intelligence solutions for process manufacturing and have been working closely with leading international industry partners to improve manufacturing efficiency, resilience, and sustainability. Backed by a highly dedicated team of world-class scientists and engineers, we aim to lead the real-world deployment of industrial intelligence in the market.

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Role Overview

As a STRELI Data Scientist, you will work at the intersection of data science and control engineering, contributing to the analysis, modelling, and optimisation of real industrial processes. The role focuses on researching and applying data-driven/machine learning-based control and optimisation methods to real-world manufacturing systems/plants. You will work closely with on-site industrial and control engineers, engaging directly with factory operations, process data, and deployed systems. This is a research-driven, hands-on startup role where your work moves beyond models and simulations into real deployments, and offers the opportunity to see advanced optimisation methods translated into measurable operational and sustainability impact.

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Key Responsibilities

·       Research, develop, and apply AI/ML-based algorithms and optimisation methods (e.g. PID, MPC) for operational and manufacturing performance

·       Research and analyse real-world industrial process data to understand system dynamics, stability, and performance

·       Research and design data-driven and ML-based optimisation approaches (e.g. optimisation, reinforcement learning) for industrial process modelling and control

·       Translate optimisation methodologies into LLM-supported and agentic workflows for industrial process analysis, recommendation, and control support.

·       Translate high-level operational objectives (CO2, Energy Reduction, Revenue increase) into actionable control and optimisation strategies

·       Collaborate with on-site industrial, control, and software engineers to deploy research outcomes into operational systems/factories

·       Develop a strong understanding of diverse industrial processes and produce clear scientific and technical documentation describing models, assumptions, methodologies, and results

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Requirements

·       Undergraduate degree or higher in Data Science, Engineering, Applied Mathematics, Computer Science, or a related discipline

·       2–5 years of relevant experience in industrial data analysis, process/industrial optimisation, control systems, or applied research (PhD research experience counts)

·       Experience working with time-series and industrial process data

·       Strong foundation in control systems, including PID or MPC strategies,  and modelling tools such as MATLAB/Simulink or similar tools.

·       Strong programming skills in Python, with experience in deep learning, machine learning, and agentic automation tools such as LangChain, LangGraph, and RAG.

·       Ability to design and evaluate models under real-world constraints, including noise, uncertainty, latency, robustness, and safety considerations

Strong analytical skills and ability to communicate technical results clearly and collaborate effectively in multidisciplinary teams (English, Mandarin, Cantonese)

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Why Join STRELI

·        Gain experience in a company that drives industrial sustainability and energy efficiency at scale, as part of a team shaping the next generation of industrial intelligence

·        Gain broad exposure across operations and business functions, working closely with major industry partners in a technology-driven environment

·        Work closely with a small, dedicated team delivering real-world industrial solutions, with opportunities to learn and make a meaningful impact in industrial intelligence

·        Join STRELI at an early stage and grow with the company, taking on real responsibility and helping shape its direction