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.

Position 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 team and 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 (both production and repair activities) more sustainable and competitive through innovation and collaboration to achieve industrial excellence.

Main Tasks & Responsibilities:

Team and data science portfolio management: Responsible for the Roadmap of the data science and manage data science ROI and Portfolio, develop requirements and ensure value and benefit the shopfloor operation, guide data science engineers to develop and review their work as well to grow their skills and experience.
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, gathering requirement, operational challenges, and opportunities for leveraging data science to drive value.
Technical capability on Data Science: Work and guide data engineers and domain experts to identify relevant data sources, extract, clean, and preprocess data for analysis and application and modeling, Mentor and develop data science and data engineering team members, fostering technical excellence, knowledge sharing, and continuous learning within the team.
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
Data Governance and Data Strategy: Define and enforce data governance standards, including data quality, data lineage, metadata management, and security. Establish data strategies that ensure reliable, scalable, and trusted data pipelines to support industrial analytics and AI initiatives.
AI/ML Lifecycle Management (MLOps):Implement and manage the end-to-end machine learning lifecycle including experimentation, versioning, CI/CD pipelines, automated model retraining, monitoring for model drift, and continuous improvement.
Change Management and AI Adoption: Drive adoption of data science solutions by collaborating with operational teams, ensuring that developed models and insights are embedded into decision-making processes and shopfloor operations.

Candidate Profile & Qualifications:

Bachelor's degree in computer science, data science, industrial engineering, or a related field; advanced degree or relevant certifications preferred.
At least 8 years proven experience in project management, specifically in leading data science or analytics projects in industrial settings.
Experience managing small team and data science portfolio
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.
Proficiency in project management methodologies and tools
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
Experience with architecting, IS/IT projects or vendors management is a plus
Strong disposition toward continuous learning, self-starter and passionate on data science to create business value
Good attitude, open mind and flexibility to adjust and promote change for continuous improvement

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.

Industrial Data Science & AI Manager