Data Scientist - Manufacturing Analytics

Visa Hunt; Singapore; On-site; SGD 110,000 - 170,000; Full time

Job summary

Visa Hunt is seeking a senior Data Scientist specialized in manufacturing analytics. The role focuses on time-series analysis with Seeq, building predictive models for maintenance, efficiency and quality improvements.

You will work closely with plant and operations teams to translate process challenges into analytics solutions, ensuring data quality and governance across datasets. The ideal candidate has 6+ years in data science, strong Python/SQL skills, and hands-on experience with industrial

Qualifications

• 6+ years of experience in Data Science / Advanced Analytics.
• Hands-on experience in manufacturing / industrial / plant environments.
• Strong working knowledge of Seeq for time-series analysis, including Seeq Workbench and Data Lab.
• Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL.
• Strong understanding of machine learning, regression, anomaly detection, predictive models.
• Statistical modeling and hypothesis-driven analysis.
• Time-series / sensor data analytics.
• Experience building and deploying predictive models for predictive maintenance, process optimization, quality and yield improvement.
• Ability to work with sensor data, process data, and operational datasets.
• Strong analytical thinking, troubleshooting, and root-cause analysis capability.

Responsibilities

• Analyze manufacturing plant and process data to identify patterns and anomalies.
• Use Seeq for time-series analysis and root-cause investigation.
• Develop and deploy machine learning models for predictive maintenance and process optimization.
• Monitor and improve model performance in production.
• Translate business challenges into data science solutions.
• Build and maintain data pipelines and analytical datasets.
• Collaborate with plant, engineering, and operations teams.
• Present insights through dashboards and stakeholder discussions.
• Ensure data quality, reliability, and governance across sources.

Skills

Data Science

Python

SQL

Time-series

Machine Learning

Troubleshooting

Root-cause analysis

Tools

Seeq

Pandas

NumPy

Scikit-learn

Job description

Requirements

6+ years of experience in Data Science / Advanced Analytics

Hands-on experience in manufacturing / industrial / plant environments

Strong working knowledge of Seeq (industrial analytics platform) for time-series analysis, including both Seeq Workbench and Data Lab (using the seeq spy library).

Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL

Strong understanding of:

Machine Learning (regression, anomaly detection, predictive models)

Statistical modeling and hypothesis-driven analysis

Time-series / sensor data analytics

Experience building and deploying predictive models for:

Predictive maintenance

Process optimization

Quality and yield improvement

Ability to work with sensor data, process data, and operational datasets

Strong analytical thinking, troubleshooting, and root-cause analysis capability

Good-to-Have Skills

Experience in industries such as:

Oil & Gas, Chemicals, Manufacturing

Knowledge of MLOps (model deployment, monitoring, pipelines)

Exposure to optimization techniques for industrial processes

Exposure to cloud platforms (Azure / AWS / GCP)

Familiarity with data visualization tools, real-time / streaming data analytics, data engineering (ETL / data pipelines / data lakes)

Roles & Responsibilities

Analyze manufacturing plant and process data to identify patterns, anomalies, and optimization opportunities

Use Seeq platform for:

Time-series analysis

Root cause investigation

Process monitoring and visualization

Develop and deploy machine learning models for:

Predictive maintenance

Process efficiency improvement

Quality / yield optimization

Work closely with plant, engineering, and operations teams to understand real-world process issues

Translate business and operational challenges into data science solutions

Build and maintain data pipelines, analytical datasets, and workflows

Monitor, evaluate, and continuously improve model performance in production

Present actionable insights through dashboards, reports, and stakeholder discussions

Ensure data quality, reliability, and governance across manufacturing data sources

Drive adoption of data-driven decision-making across plant operations