Data Scientist - Manufacturing Analytics

RISKDATA CONSULTING PTE. LTD.; Singapore; On-site; SGD 90,000 - 120,000; Full time

Job summary

RISKDATA CONSULTING PTE. LTD. is seeking a seasoned Data Scientist with 6+ years of experience to transform plant data into actionable insights.

You'll work with Seeq for time-series analysis, build predictive models in Python, and collaborate with manufacturing teams to identify maintenance and process optimization opportunities. You will develop data pipelines, monitor model performance in production, and present dashboards to stakeholders.

Qualifications

• 6+ years of experience in Data Science / Advanced Analytics.
• Hands-on experience in manufacturing / industrial / plant environments.
• Proficiency in Seeq for time-series analysis (Workbench and Data Lab).
• Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL.
• Strong skills in ML (regression, anomaly detection, predictive models).
• Experience building and deploying predictive models for predictive maintenance, process optimization, quality/yield improvements.

Responsibilities

• Analyze manufacturing plant and process data to identify patterns, anomalies, and optimization opportunities.
• Use Seeq for time-series analysis, root cause investigation, and process monitoring/visualization.
• Develop and deploy machine learning models for predictive maintenance and process efficiency improvement.
• Work with plant, engineering, and operations teams to understand real-world process issues and translate them into data science solutions.
• Build and maintain data pipelines, analytical datasets, and workflows; monitor model performance in production.

Skills

Time-series analysis

Root-cause analysis

Analytical thinking

Predictive modeling

Data visualization

Tools

Seeq

Python

SQL

Pandas

NumPy

Scikit-learn

Seeq Data Lab

Job description

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