Freelancer profile translated to English.

Description

I help manufacturers leverage their data to improve the performance and profitability of their processes.

My strength: a dual expertise in process engineering and data science, allowing me to analyze both field challenges and data potential.

For 6 years, I have been involved in the process industry on projects such as:

→ Decision support for reagent selection & validation of raw material consumption data on a reaction process (chemical industry)

→ Analysis of the influence of operating parameters on the yield of a biomass boiler and recommendations (energy production)

→ Modeling and real-time monitoring of continuous industrial crystallizers for operational guidance (food industry)

Here are the areas in which I intervene:

● Defining the scope of digitalization projects

Identifying business needs, prioritizing use cases, roadmap

● Data centralization, contextualization, and validation

Automated collection, data cleaning, sensor anomaly handling

● Industrial process modeling and simulation

Mass and energy balances, equipment modeling (crystallizers, reactors, heat exchangers, ...), migration of Excel/VBA/MatLab models to Python

● Performance monitoring

Real-time dashboards, automated reporting, operational guidance

● Process optimization

Identifying improvement levers, recommendations

● Team upskilling

Training, workshops, documentation, post-deployment support

Support missions (studies, development) or integrated services, depending on project needs.

My approach adapts to the digital maturity level of each site and its operational constraints. I offer an initial diagnostic discussion to define your needs before any intervention.

Industry field of expertise

Languages

French
Native or bilingual
English
Fluent

Workplace preferences

Can work on-site

Lyon (up to 50km), Lille (up to 50km), Strasbourg (up to 50km), Paris (up to 50km), Rouen (up to 50km)

Butachimie
Data Process / Industrial Digitalization Consultant
CHEMICAL
June 2025 - Today (1 year)
Mulhouse, France
Mission carried out on-site in Chalampé.

🟪 Data Analysis & Valorization → Design of dashboards, advanced analyses, and process modeling to transform information into KPIs.
🟪 Structuring & Documentation → Writing user guides, formalizing needs, best practices for sustainable adoption.
🟪 Project Management & Coordination → Planning, meeting facilitation, use case piloting.
Seeq Microsoft Power BI Python Data Analysis Data Validation
INEVO
Data Process Consultant
CHEMICAL
January 2025 - June 2025 (5 months)
Lyon, France
INEVO (End client: chemical industry)

🎯Project Objective
Optimize the production yield of a chemical plant by identifying critical operating conditions
Provide field teams with tools to monitor and optimize yield.

🔧Missions Performed
✔ Needs analysis and scoping
▪ Understanding business needs
▪ Clarifying performance objectives and indicators to track

✔ Industrial Data Validation & Enrichment
▪ Detection and analysis of data anomalies (outliers, sensor inconsistencies, etc.)
▪ Automated correction via Python and/or OIAnalytics configuration
▪ Reconstruction of missing data from business calculations

✔ Real-time Monitoring & Performance Visualization
▪ Design of dashboards in OIAnalytics to monitor raw material consumption in real-time
▪ Adaptation of visualizations to field team usage

✔ Production Yield Analysis and Optimization
▪ Analysis of operating conditions influencing yield
▪ Recommendations for industrial performance optimization

📊Results Achieved
▪ Validated and usable raw material consumption data.
▪ Operational dashboards tailored to user needs.
▪ Decision support matrix for reagent selection.

🧰Tools
OIAnalytics · Python · Office 365
Python OIAnalytics Data Analysis Data Validation Scientific Computing
ForTech Conseil
Digitalization Consultant for Industry
CHEMICAL
January 2025 - Today (1 year and 5 months)
Lyon, France
✔ Defining the Scope of Digitalization Projects
▪ Assessment of companies' current digitalization level.
▪ Identification of improvement areas and operational needs
▪ Development of a strategic roadmap towards Industry 4.0.

✔ Centralization and Validation of Industrial Data
▪ Implementation of systems to collect, clean, and structure industrial data.
▪ Data validation to ensure their quality and accuracy.

✔ Process Modeling and Simulation
▪ Creation of tailor-made models for industrial processes: Mass and energy balances, Chemical reaction modeling, etc.
▪ Migration and modernization of existing models (Excel, VBA, MatLab, etc.) to Python.

✔ Real-time Monitoring
▪ Automated reporting, increasing time available for high-value-added tasks.
▪ Visualization of key performance indicators (KPIs) for faster decision-making.

✔ Industrial Performance Analysis and Optimization
▪ Optimization of process operating conditions to maximize facility efficiency.
▪ In-depth data analysis to optimize industrial performance indicators (KPIs).

✔ Team Support and Training
▪ Training teams to upskill and leverage implemented digital solutions.
Python Process Engineering Data Analysis KPI Definition and Monitoring Industrial Data Validation