Lead Scientist, Modeling & Simulation
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What you'll do
- Extracting insights from large, heterogeneous datasets.
- Collecting, cleaning, and transforming large datasets.
- Building statistical / ML models.
- Identifying patterns and trends for business or engineering decisions.
- Delivering predictive models, process optimization, and decision support.
- Communicating insights to cross-functional stakeholders.
- Work with RD, process engineers, equipment engineers and IT in the following areas and translate RD/engineering problems into data science frameworks.
- Support the integration of datasets from multiple engineering/RD domains for data science applications (statistics, AI/ML, time-series analysis … ).
- Develop models for yield, quality, and defect prediction to support root cause analysis and anomaly detection for improvement in process control and productivity.
- Build predictive models and deploy ML pipelines in production environments to support the development of AI-enabled solutions.
- Develop physics-informed ML models or design new modeling approaches integrating computational simulation data and experimental data to solve scientific /engineering problems.
What they're looking for
- Master’s degree or PhD in data science, computer science, industrial engineering, chemical engineering, chemistry, physics or related fields.
- Proficiency in collecting, cleaning and transforming large datasets and building data models based on statistics, AI/ML etc.
- Proficiency in tools such as Python, R, TensorFlow, PyTorch, SQL or Tableau.
- Good communication skills in both Mandarin and English.
- Experiences in chemical manufacturing, semiconductor manufacturing or related fields.
- Ability to do research independently to identify optimal solutions / approaches for target scientific or engineering problems.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Job Title:
Lead Scientist, Modeling & SimulationJob Description:
The Role: (L4) Data Scientist at TTC
A bridge between RD/manufacturing processes and data/ML
Responsible for extracting insights from large, heterogeneous datasets
Collecting, cleaning, and transforming large datasets
Building statistical / ML models
Identifying patterns and trends for business or engineering decisions
Delivering predictive models, process optimization, and decision support
Communicating insights to cross-functional stakeholders
In this role you will:
Work with RD, process engineers, equipment engineers and IT in the following areas and translate RD/engineering problems into data science frameworks.
Support the integration of datasets from multiple engineering/RD domains for data science applications (statistics, AI/ML, time-series analysis…).
Develop models for yield, quality, and defect prediction to support root cause analysis and anomaly detection for improvement in process control and productivity.
Build predictive models and deploy ML pipelines in production environments to support the development of AI-enabled solutions.
Develop physics-informed ML models or design new modeling approaches integrating computational simulation data and experimental data to solve scientific /engineering problems.
Traits we believe make a strong candidate:
Good communication skills in both Mandarin and English.
A team player able to work with different stakeholders in diversified task forces.
Experiences in chemical manufacturing, semiconductor manufacturing or related fields.
Master’s degree or PhD in data science, computer science, industrial engineering, chemical engineering, chemistry, physics or related fields.
Proficiency in collecting, cleaning and transforming large datasets and building data models based on statistics, AI/ML etc.
Proficiency in tools such as Python, R, TensorFlow, PyTorch, SQL or Tableau.
Ability to do research independently to identify optimal solutions / approaches for target scientific or engineering problems.
Your success will be measured by:
Able to collaborate with diversified team members or stakeholders and deliver quality results on time.
Able to probe stakeholders’ needs and translate their problems into data science frameworks.
Accountable for delivering high quality data science solutions for target problems.
Dedicated to improving solution quality continually.
Determined to attack difficult challenges with perseverance and creativity.
Passionate to explore and develop data science applications in different areas.
Curious to dive deeper into data science domain and keep abreast of latest developments.
Motivated to acquire new knowledge and skills and apply them in solution development.
Able to accept coaching and grow himself/herself continually with a growth mindset.
Company
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