Senior Industrial Data Scientist/Statistical Modeling Lead

Data Science

Machine Learning

Python

R

SQL

Responsibilities

• Statistical Modeling & Analytics (40%)
• Time-Series Forecasting: Build robust forecasting models using ARIMA, exponential smoothing, and regression methods
• Statistical Process Control: Implement control charts, capability analysis, and process monitoring systems
• Predictive Maintenance: Develop survival analysis and degradation models for equipment failure prediction
• Anomaly Detection: Create statistical outlier detection and control limit-based monitoring systems
• Model Validation: Ensure statistical rigor, hypothesis testing, and confidence intervals for all predictions
• Technical Leadership (35%)
• Define Analytics Strategy: Lead the technical approach for industrial forecasting and optimization
• Production Systems: Ensure models are reliable, explainable, and deployable in industrial environments
• Team Mentoring: Guide junior analysts and engineers in statistical methods and industrial applications
• Quality Assurance: Establish validation processes and accuracy benchmarks for all models
• Tool Selection: Choose appropriate statistical software and deployment technologies
• Business Impact & Domain Expertise (15%)
• Manufacturing Domain: Understand and model complex manufacturing processes, failure modes, and quality parameters
• ROI Quantification: Translate ML predictions into measurable business value (cost savings, quality improvements)
• Client Collaboration: Work directly with manufacturing engineers and plant managers to understand requirements
• Model Validation: Ensure model accuracy and reliability in real-world manufacturing environments
• Team Building & Operations (10%)
• Team Leadership: Build and lead a high-performing data science team
• Process Establishment: Define ML development processes, model governance, and quality standards
• Cross-functional Collaboration: Work closely with data engineering, software engineering, and domain experts
• Knowledge Sharing: Establish documentation standards and knowledge transfer processes
  

Must Have

• 7+ years of hands-on experience in data science and machine learning
• 3+ years in leadership or senior individual contributor roles
• 2+ years experience with real-time ML systems or industrial applications
• Statistical Methods & Analytics
• Time-Series Analysis: ARIMA, exponential smoothing, seasonal decomposition, trend analysis, Box-Jenkins methodology
• Statistical Process Control: Control charts (X-bar, R, CUSUM), process capability analysis, Six Sigma methods
• Regression Analysis: Linear/non-linear regression, logistic regression, robust regression methods
• Survival Analysis: Kaplan-Meier, Cox regression, Weibull analysis for equipment failure prediction
• Hypothesis Testing: t-tests, ANOVA, chi-square, non-parametric tests, power analysis
• Industrial Statistics: DOE (Design of Experiments), response surface methodology, reliability analysis
• Programming & Tools
• Python: Advanced proficiency (pandas, numpy, scipy, statsmodels, scikit-learn)
• R: Statistical analysis (preferred for advanced statistical modeling)
• SQL: Complex queries, time-series databases (InfluxDB, TimescaleDB)
• Statistical Software: Experience with SAS, SPSS, Minitab, or JMP
• Visualization: Statistical plots, process control charts (matplotlib, ggplot2, Tableau)
• Industry Experience
• Manufacturing/Industrial: 3+ years experience with manufacturing processes, quality control, or industrial operations
• Statistical Applications: Applied statistics in manufacturing, process industries, or quality improvement
• Industrial Data: Working with sensor data, process parameters, equipment monitoring systems
• Quality Systems: Six Sigma, Lean Manufacturing, ISO 9001, or similar quality frameworks

Key Skills

• Data Science
• Machine Learning
• Python
• R
• SQL