Industrial Data Science & AI Engineer

Job description

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Industrial Data Science & AI Engineer
role at
Thales
Location: Singapore, Singapore
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
Project Planning: Develop comprehensive project plans, defining scope, objectives, deliverables, timelines, resource allocation, and budget estimates for industrial data science projects.


Stakeholder Engagement: Collaborate with stakeholders to understand business needs, operational challenges, and opportunities for leveraging data science to drive value.


Data Acquisition and Preparation: Work with data engineers and domain experts to identify relevant data sources, extract, clean, and preprocess data for analysis and modeling.


Data Analysis and Modeling: Lead data exploration, statistical analysis, and machine learning model development to uncover insights, patterns, and trends in industrial data.


Model Deployment: Oversee the deployment of data science models into production environments, ensuring scalability, reliability, and integration with existing systems.

Deploy standards defined and contribute to their improvements.


Performance Monitoring: Establish key performance indicators (KPIs) and monitoring mechanisms to track the performance and effectiveness of deployed models over time with business value generated.


Cross-Functional Collaboration: Coordinate with cross-functional teams, including data scientists, engineers, IT specialists, and business analysts, to ensure alignment and synergy in project execution.


Risk Management: Identify and mitigate potential risks and challenges associated with data science projects, such as data quality issues, algorithmic bias, and model interpretability.


Quality Assurance: Implement quality control measures and validation procedures to ensure the accuracy, robustness, and reliability of data science solutions.


Documentation and Reporting: Maintain detailed documentation of project activities, methodologies, findings, and outcomes, and provide regular progress updates and reports to stakeholders.


Business Value Delivery: Define, measure and keep track of business value deliverables link to the project ROI.


Technical Leadership: Drive the design, development, and optimization of scalable data pipelines, APIs, and data platforms to support advanced analytics, AI, and business intelligence use cases.


Dashboard & Visualization Solutions: Lead the development of enterprise-grade dashboards using Flask (or equivalent frameworks), ensuring usability, performance, and integration with data science models.


Data for Digital Twin & Simulation: Architect and oversee the creation of data inputs for digital twin environments, enabling predictive simulations and real-time monitoring by integrating structured/unstructured inputs (JSON, XML, APIs).


AI & Chatbot Integration: Design and implement intelligent assistant solutions, leveraging Retrieval-Augmented Generation (RAG) and related AI techniques to enhance knowledge discovery and automation.


Data Strategy & Standards: Define best practices for data engineering, quality assurance, monitoring, and governance, ensuring compliance with enterprise and security standards.


Collaboration & Mentorship: Work closely with cross-functional teams (data scientists, software engineers, product owners) while mentoring junior engineers to raise the team’s technical capability.


Requirements
Bachelor's degree in computer science, data science, industrial engineering, or a related field.


Proven experience in project management, specifically in leading data science or analytics projects in industrial settings.


Experiences on requirement gathering, scoping, data mapping and data driven improvement, digital transformation projects to deliver business objectives are plus.


Strong technical proficiency in data science tools and techniques, including architecting, statistical analysis, machine learning, predictive modeling, and data visualization.


Experience with industrial data sources, such as sensor data, time-series data, SCADA systems, and IoT devices.


Excellent leadership, communication, and stakeholder management skills, with the ability to engage and influence both internal and external stakeholders at all levels of the organization.


Knowledge of industrial processes, manufacturing operations, and relevant industry standards and regulations.


Familiarity with data governance, privacy, and security best practices in industrial environments.


Experience with process optimization, continuous improvement, and lean manufacturing principles is a plus.


Proven track record of dashboarding and visualization (e.g., PowerBI, Flask, Plotly/Dash, or integration with BI tools) for decision-making support.


Experience with digital twin simulation and real-time data integration, including structured/unstructured formats (JSON, XML) and APIs.
Exposure to AI-driven solutions, especially chatbot development and Retrieval-Augmented Generation (RAG).


Excellent communication and stakeholder management skills, with the ability to present complex technical concepts to non-technical audiences.


Other Information
Work Location: Changi North Rise
Working Days: Monday - Friday
Company transport provided from designated MRT stations.


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.


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