Industrial Data Science

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 linked 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.

• 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.

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