Data Scientist - Manufacturing Analytics

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