Industrial Data Science & AI Engineer
Industrial Data Science & AI Engineer
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Job Description
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 were 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 2000 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 were 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 teams technical capability.
Requirements:
- Bachelors 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 were committed to fostering a workplace where respect trust collaboration and passion drive everything we do. Here youll 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.
Employment Type
Full-Time
