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Senior Industrial Data Scientist/Statistical Modeling Lead- SIDS300925EN (7+ years)

Pune, India

Our focus revolves around elevating technology-driven enterprises to new heights. However, it's important to understand that our scope at Trinesis encompasses more than just software development. Our objective is to provide comprehensive assistance to startups and enterprises throughout every phase of their journey.

As our Team expands, we're actively seeking new talent. Currently, we're on the lookout for a skilled Senior Industrial Data Scientist to lead the development of robust statistical models for manufacturing forecasting and optimization. This is a hands-on technical leadership role focused on applying proven statistical methods, time-series analysis, and industrial process knowledge to solve real manufacturing problems with measurable business impact.

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

What's great in the job?

Great team of smart people, in a friendly and open culture.
Competitive salary and benefits package.
Opportunity for professional growth and advancement.
Dynamic and collaborative work environment.
Flexible working hours and remote work options.
Various learning opportunities and training programs.

Our Product

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What We Offer

Each employee has a chance to see the impact of his work. You can make a real contribution to the success of the company.
Several activities are often organized all over the year, such as weekly sports sessions, team building events, monthly drink, and much more