8–10 years of overall data science experience required
2-4 years working in manufacturing domain
Hands-on experience in shop floor operations, production planning, and systems including MES, SCADA, and ERP
Proficient in industrial protocols (OPC-UA, MQTT, Modbus)
Ability to bridge OT/IT systems for real-time data extraction
Applied experience with OEE, Six Sigma, SPC, and lean methodologies to drive measurable gains in yield, uptime, and efficiency
Skilled in building scalable cloud data pipelines for high-volume manufacturing and IoT data using Spark, Kafka, Airflow, and Delta Lake
Strong SQL and Python proficiency with hands-on experience in medallion/lakehouse architectures on Databricks, Snowflake, AWS, or Azure
Proven track record building and deploying ML models for predictive maintenance, anomaly detection, demand forecasting, and root cause analysis
Proficient in scikit-learn, TensorFlow, or PyTorch
Experience moving models from prototype to production in industrial environments
Strong grounding in statistical methods — time series, regression, clustering, and hypothesis testing applied to manufacturing quality problems
Experience designing A/B experiments and simulations to validate process changes and quantify business impact before full deployment

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data sciencemachine learningcloud data pipelinesSQLPythonSparkKafkaAirflowDelta Lakestatistical methods

Soft Skills

collaborationcommunicationstakeholder engagementproblem-solvingrecommendation translation