Industrial Data Science

Overview 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. In Singapore, Thales has been a trusted partner since 1973, with 2,000 employees across three local sites, delivering solutions across aerospace, defence and security, and digital identity and cybersecurity sectors. This position is for an Industrial Data Science & AI Engineer within Thales Avionics (AVS) in Singapore, responsible for leading data-driven projects aimed at optimizing industrial processes, improving efficiency, and driving innovation. This role blends project management, technical data science and analytics proficiency, and a deep understanding of industrial operations. The role initiates, influences, and drives stakeholders to emulate, synchronize and connect to help Thales AVS become 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 deployment of data science models into production environments, ensuring scalability, reliability, and integration with existing systems; contribute to deploying and improving defined standards. Performance Monitoring: Establish KPIs and monitoring mechanisms to track the performance and business value of deployed models over time. Cross-Functional Collaboration: Coordinate with cross-functional teams to ensure alignment and synergy in project execution. Risk Management: Identify and mitigate risks such as data quality issues, algorithmic bias, and model interpretability. Quality Assurance: Implement quality control measures and validation procedures to ensure accuracy, robustness, and reliability of solutions. Documentation and Reporting: Maintain detailed documentation of project activities and provide regular progress updates to stakeholders. Business Value Delivery: Define and measure business value deliverables linked to project ROI. Technical Leadership: Drive design, development, and optimization of data pipelines, APIs, and data platforms to support advanced analytics, AI, and BI use cases. Dashboard & Visualization Solutions: Lead enterprise-grade dashboards using Flask or equivalent frameworks, ensuring usability and integration with models. Data for Digital Twin & Simulation: Architect data inputs for digital twin environments to support predictive simulations and real-time monitoring with structured/unstructured inputs (JSON, XML, APIs). AI & Chatbot Integration: Design intelligent assistant solutions leveraging Retrieval-Augmented Generation (RAG) and related AI techniques. Data Strategy & Standards: Define best practices for data engineering, quality assurance, monitoring, and governance, ensuring compliance with enterprise and security standards. Collaboration & Mentorship: Work with cross-functional teams and mentor 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, leading data science or analytics projects in industrial settings. Experience in requirements gathering, scoping, data mapping, and data-driven improvement; digital transformation projects to deliver business objectives is a plus. Strong technical proficiency in data science tools and techniques, including architecture, statistical analysis, machine learning, predictive modeling, and data visualization. Experience with industrial data sources (sensor data, time-series, SCADA, IoT). Excellent leadership, communication, and stakeholder management skills. Knowledge of industrial processes, manufacturing operations, and relevant standards/regulations. Familiarity with data governance, privacy, and security best practices in industrial environments. Experience with process optimization, continuous improvement, and lean manufacturing is a plus. Proven track record of dashboarding/visualization (PowerBI, Flask, Plotly/Dash) for decision-making support. Experience with digital twin simulation and real-time data integration (JSON, XML, APIs). Exposure to AI-driven solutions, especially chatbot development and Retrieval-Augmented Generation (RAG). Excellent communication and stakeholder management skills, with 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. #J-18808-Ljbffr