Data Science & AI Engineer (1 Year Contract)
AI in this role
Thales is a global technology leader trusted by governments, institutions, and enterprises to tackle their most demanding challenges. From quantum applications and artificial intelligence to cybersecurity and 6G innovation, our solutions empower critical decisions rooted in human intelligence. Operating at the forefront of aerospace and space, cybersecurity and digital identity, we’re driven by a mission to build a future we can all trust.
In Singapore, Thales has been a trusted partner since 1973, originally focused on aerospace activities in the Asia-Pacific region. With 2,000 employees across three local sites, we deliver cutting-edge solutions across aerospace (including air traffic management), defence and security, and digital identity and cybersecurity sectors. Together, we’re shaping the future by enabling customers to make pivotal decisions that safeguard communities and power progress.Summary:
Thales Avionics (AVS) in Singapore consists of manufacturing and repair activities for aircraft OEM and airlines respectively.
This position is responsible for leading data-driven projects aimed at optimizing industrial processes, improving efficiency, and driving innovation. They oversee the end-to-end project lifecycle, from requirements gathering and data analysis to model development, deployment, and performance monitoring.
This role holds a blend of project management expertise, technical proficiency in data science and analytics, and a deep understanding of industrial operations. He/She will be the initiator, influencer and driving the stakeholders to emulate, synchronize and connect to make Thales AVS more sustainable and competitive through innovation and collaboration to achieve industrial excellence.
Responsibilities:
- Work closely with data scientists to understand their needs and provide the data they require.
- Work closely with domain stakeholders to translate business problem into analytical/AI Modelling/ML solutions and clear metrics.
- Design, build, and maintain scalable and reliable data pipelines, software and interfaces with various data sources.
- Involve and manage end-to-end solution cycle: discovery -> feature engineering -> Modelling/experimentation/POC/Pilot -> deployment -> Monitoring -> propose next steps
- Serve as a technical bridge, collaborating closely with cross-functional teams to ensure alignment and smooth execution of projects.
- Collect and process the data in a suitable format to support various analytical requirements.
- Oversee data gathering, cleaning, and preprocessing, ensuring data quality and readiness for analysis. Work closely with data engineers and other experts to acquire data from industrial sources, including Test benches , IoT devices, sensors, and SCADA systems.
- Perform and integrate data quality checks to identify and correct errors or discrepancies.
- Lead exploratory data analysis and machine learning model development to uncover insights, trends, and optimization opportunities.
- Design models tailored to meet specific project goals, and continually iterate to ensure optimal performance and alignment with business value objectives.
- Create and maintain documentation related to data flows and model, transformations applied, and validation procedures.
- Establish and monitor performance metrics to track the success and ROI of deployed tools and models. Ensure deployed solutions are reliable, scalable, and aligned with best practices.
- Dashboard Development: Design and implement interactive dashboards using PowerBi, Flask (or similar web frameworks), ensuring intuitive visualization of complex datasets for business and technical stakeholders.
Data for Digital Twin & Simulation: Build and maintain data input for digital twin environments by modeling processes, simulating outcomes, and integrating live data streams through JSON, XML, and APIs.
AI/Chatbot Solutions: Develop and support intelligent chatbot systems leveraging Retrieval-Augmented Generation (RAG) and related AI technologies to improve knowledge accessibility and user interactions.
Requirements:
- Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
- Proficiency in Python (or similar languages), SQL, and experience with big data technologies like Hadoop, Spark, or similar.
- Hands-on experience with SQL and NoSQL data stores, such as PostgreSQL.
- Strong analytical skills and the ability to work with large datasets.
- Dashboarding and Web Technologies: Hands-on experience with PowerBI ,Flask (or similar frameworks),building interactive dashboards, and integrating visualization tools.
- Data for Digital Twin Simulation: Experience in modeling, simulating, and ingesting data through JSON, XML, and APIs for real-time or near-real-time systems.
- Chatbot and AI-Powered Solutions: Familiarity with Retrieval-Augmented Generation (RAG) and other chatbot frameworks for knowledge-based interaction systems
Other Information:
- Work Location: Changi North Rise
- Working Days: Monday - Friday
- Company transport provided from designated MRT stations.
- 1 Year Contract, able to renew/extend based on business requirements
At Thales, we’re committed to fostering a workplace where respect, trust, collaboration, and passion drive everything we do. Here, you’ll feel empowered to bring your best self, thrive in a supportive culture, and love the work you do. Join us, and be part of a team reimagining technology to create solutions that truly make a difference – for a safer, greener, and more inclusive world.
How we score this
Data Science & AI Engineer (1 Year Contract) at Thales scores 66 out of 100 on AI centrality, which makes it AI Level 3 of 4 (Works on AI) on this board. The level measures how much of the work is AI, not seniority.
AI Level 3. The daily work is on or around AI systems, without necessarily building the model: remove AI and the job is hollow.
- AI Level 480 to 100
- AI Level 360 to 79
- AI Level 240 to 59
- AI Level 10 to 39
Bands come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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Skills and AI tools this role asks for
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- How would you design a retrieval step so the model answers from real data instead of guessing?
- Describe a typical day in a role like this one: which parts run through AI directly?
- If you removed AI from this role, what would be left, and how do you decide what still needs a human?
Adapt your resume
- List these exact terms on your resume: Rag. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.
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