About the role
AI summarisedLead data-driven projects within Thales Avionics (AVS) in Singapore, focusing on optimizing industrial processes, improving efficiency, and driving innovation across manufacturing and repair activities. This role requires a blend of team leadership, project management expertise, and deep technical proficiency in data science to deliver measurable business value.
Aerospace & DefenseOnsite
Key Responsibilities
- Manage the data science roadmap and portfolio, ensuring data science ROI and developing requirements that benefit shopfloor operations.
- Develop comprehensive project plans, defining scope, objectives, deliverables, timelines, resource allocation, and budget estimates for industrial data science projects.
- Collaborate with stakeholders to deeply understand business needs, gather requirements, and identify opportunities for data science value creation.
- Guide data engineers and domain experts in identifying, extracting, cleaning, and preprocessing relevant industrial data for analysis and modeling.
- Lead data exploration, statistical analysis, and machine learning model development to uncover actionable insights from industrial data.
- Oversee the deployment of developed models into production environments, ensuring scalability, reliability, and integration with existing systems.
- Establish KPIs and monitoring mechanisms to track the performance and business value generated by deployed models over time.
- Coordinate with cross-functional teams including data scientists, engineers, IT specialists, and business analysts to ensure project synergy.
- Identify and mitigate risks associated with data science projects, including data quality issues, algorithmic bias, and model interpretability.
- Maintain detailed documentation of project activities, methodologies, findings, and provide regular progress reports to stakeholders.
Requirements
- Proven ability to lead data-driven projects in an industrial setting.
- Expertise in data science lifecycle management, from requirements gathering to deployment and monitoring.
- Strong project planning skills including scope definition, timeline management, and resource allocation.
- Proven ability to engage with technical and non-technical stakeholders to drive business value.
- Proficiency in guiding data engineering on data extraction, cleaning, and preprocessing.
- Experience leading statistical analysis and machine learning model development.
- Understanding of deploying models into production environments.
- Ability to define and track business value deliverables linked to project ROI.