Job Description

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?

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+ 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.

What We Offer

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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

Perks

A full-time position


Attractive salary package.

Trainings

12 days / year, including


6 of your choice.

Sport Activity

Play any sport with colleagues,


the bill is covered.

Eat & Drink

Fruit, coffee and


snacks provided.