Senior Industrial Data Science Specialist

5 days ago

Singapore beBeeData Full time

Industrial Data Scientist Role

About the Role:

  • Project Development: Develop comprehensive project plans defining scope, objectives, deliverables, timelines, resource allocation, and estimates for industrial data science projects.
  • Business Alignment: Collaborate with stakeholders to understand business needs, operational challenges, and opportunities for leveraging data science to drive value.
  • Data Preparation: Work with data engineers and domain experts to identify relevant data sources, extract, clean, and preprocess data for analysis and modeling.
  • 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.
  • Performance Monitoring: Establish key performance indicators (KPIs) and monitoring mechanisms to track the performance and effectiveness 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 potential risks and challenges associated with data science projects.
  • 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 providing regular progress updates and reports to stakeholders.
  • Value Delivery: Define measure and keep track of business value deliverables linked to 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.
  • 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 while mentoring junior engineers to raise the team's technical capability.

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