About ST Engineering
ST Engineering is a global technology, defence, and engineering group with offices across Asia, Europe, the Middle East, and the U.S., serving customers in more than 100 countries. The Group uses technology and innovation to solve real-world problems and improve lives through its diverse portfolio of businesses across the aerospace, smart city, defence, and public security segments. Headquartered in Singapore, ST Engineering ranks among the largest companies listed on the Singapore Exchange.
Our history spans more than 50 years, and our strategy is underpinned by our core values – Integrity, Value Creation, Courage, Commitment and Compassion. These 5 core values guide every aspect of our business and are embedded in our ST Engineering culture – from the people we hire, to working with each other, to our partners and customers.
About our Line of Business – Training & Simulation Systems
Our Training & Simulation Systems delivers learning, training, and simulation solutions and systems that elevate training experiences; offer immersive technologies that transform learning and training; along with training support and consultancy services for aerospace, land, maritime, public safety and security, and commercial segments. We provide comprehensive end-to-end solutions, from design and development to seamless implementation, maintenance, and ongoing operations.
Together, We Can Make A Significant Impact
The Data Scientist will design, develop and implement cohesive data integration and advanced analytics solutions involving both structured and unstructured data. The role supports mission‑critical initiatives, including predictive modelling, forecasting, operations research (optimization), text mining and network analytics, particularly within regulated and public‑sector environments.
Be Part of Our Success
- Primarily responsible for applying the skills and knowledge gained about data analysis, analytics, data science to ensure the successful delivery of client engagements and initiatives within our Data and Analytics practice.
- Develop and manage the end-to-end lifecycle of analytics projects from requirement gathering, data scoping, modelling to production (model deployment and monitoring).
- Lead or support data requirement and analytics use‑case workshops with business and technical stakeholders, translating business needs into clear analytical, data, and success metrics specifications.
- Attend and assist in facilitating project meetings / workshops with client stakeholders.
- Propose, implement, and validate data science models, ensuring functional and non-functional requirements such as explainability, fairness, scalability, security, integration and operational costs.
- Participate actively in software development processes and best practices, documentation of requirements and software codes during the software development lifecycle.
- Produce high quality client‑ready deliverables/document, with-ready-to-use content.
- Independently drive assigned modules or workstreams with minimal supervision in a fast‑paced project environment.
- Prepare user requirements, data development artefacts and technical documentation in accordance with governance and audit requirements.
- Proactively research client business context, industry trends and functional domains, including public sector and government ecosystems, to stay current and relevant.
- Contribute to the development of reusable project assets such as templates, analytical frameworks, processes, reports and presentation materials.
Qualities We Value
- At least 4 years’ experience in data analytics and data science fields.
- Proven experience in data processing, feature selection, hyper-parameter optimization, model validation and visualization.
- Proven experience in AWS SageMaker, Python (e.g., Pandas, NumPy/SciPy, Scikit-Learn, XGBoost, pyspark, etc) and other related tools.
- Experience with Agentic AI/Generative AI (Large Language Model (LLM)–based solutions, including Retrieval‑Augmented Generation, knowledge assistants, document intelligence, and conversational analytics), and modern data platform is an advantage.
- Preferred: hands‑on experience in data engineering, including data ingestion, transformation, pipeline development and working with data platforms or warehouses.
- Strong SQL skills with experience working on relational data models and large datasets.
- Experience in production software engineering routines such as test-driven development, code versioning with Git, conducting code reviews, and CI/CD.
- Familiar with object-oriented programming concepts and their application to data science pipelines.
- Demonstrated ability to engage business stakeholders and lead data or analytics requirement workshops, translating complex business problems into actionable data solutions.
- Deep and eager interest in emerging technologies and the ability to leverage the technologies into solutions to meet our strategic and operational client needs.
- A self-starter with an analytical approach to problem solving.
- A client-centric, outcome driven and quality focused team player.
- Detailed oriented and is able to work in fast paced and agile environment.
- Excellent communication skills; both in written and spoken English.
Our Commitment That Goes Beyond the Norm
- An environment where you will be working on cutting-edge technologies and architectures
- Safe space where diverse perspectives are valued, and everyone’s unique contributions are celebrated
- Meaningful work and projects that make a difference in people’s lives
- A fun, passionate and collaborative workplace.
- Competitive remuneration and comprehensive benefits
