Data
Prepare, clean and analyse data with Pandas and SQL.
EXPERIENCE 01 · DATA & ARTIFICIAL INTELLIGENCE
LYON · DATA & AI2024 — 2025
AI Project Manager
TC Concept · Lyon
I worked on two complementary data initiatives: a Pricing analysis of quote conversion and a Power BI rebuild of management-control reporting, backed by a central data cube for TC Concept agencies.
PythonPandasscikit-learnSQLPower BIBusiness analysis
01 — RESPONSIBILITIES
Connect data
to decisions.
My role covered Data & AI project tracking, business analysis and collaboration with accounting, IT, data governance and technical teams.
Prediction
Develop predictive models with Python, Pandas and scikit-learn.
Reporting
Create Power BI dashboards to track indicators and support decisions.
ACTIVITY 01 — MANAGEMENT CONTROL
POWER BI REPORTING REBUILD
Replace manual Excel routines
with refreshed reporting.
The Power BI work covered two needs. First, rebuilding the Excel charts agency managers used for performance tracking and data governance. Second, a deeper budget-management overhaul: an Excel workbook with many tables, formulas, VBA macros and different data sources. I helped bring in and format the data, pre-calculate some elements and simplify the logic into a coherent, sustainable reporting tool.
The resulting tool is automatically refreshed and provides a common reporting basis for all TC Concept agencies. I also supported users as they became familiar with the new Power BI views; this was onboarding support, not a formal training course.
Manual Excel workflow
Slow file handling, repeated transformations and corrections at each reporting cycle.
Central data cube
Bring together the data needed for management control and structure a shared source for analysis.
Power BI across agencies
Rebuild management-control views in an automatically refreshed tool shared across TC Concept agencies.
Data sources→Central cube→Power BI model→Refreshed management views
Explore the Power BI project ↗ACTIVITY 02 · PRICING PROJECT · STAINLESS STEEL PIPES
Read each customer’s
position in the margin range.
The chart relates current customer margin to conversion rate in the Stainless Steel Pipes product family. The report’s markers help compare the group and guide pricing adjustments.
Revenue-weighted centreCustomers with a larger contribution to revenue have greater influence on the family’s centre.
Unweighted centreSimple average of customer positions; each customer has equal weight.
Target zoneBlue dotted lines frame a zone around the centre and help set adjustment steps.
Acceptance thresholdMinimum selling margin is set according to deal size and the customer’s company size.
INTERACTIVE READING GUIDE
Four zones to guide the analysis.
Hover, focus or select a zone to read its interpretation. The diagram explains the business logic; the actual thresholds come from the margin rules.
CONVERSION RATE
ZONE READINGSelect a quadrant
The four zones combine margin level and conversion rate to make the pricing strategy easier to read.
CURRENT MARGIN
FROM ANALYSIS TO ACTION
A clear decision aid.
The scatter plot locates customers within their product family; the surface translates those positions into proposed adjustment steps. Acceptance thresholds preserve the margin rules for each deal and company size.
Customer position→Business zone→Margin step
CASE STUDY
Quote conversion
prediction.
The Pricing report presents model iterations and category indicators. The dedicated page offers an anonymised visual reading of the project analysis.
Explore activity 02 · Pricing ↗SKILLS USED
Analysis, delivery & AI.
Skills applied across both TC Concept activities: management reporting in Power BI and the Pricing analysis.
Prepare and model
- Python, Pandas, scikit-learn
- Machine Learning and classification
- Data preparation and feature creation
- LightGBM, F1 / AUC analysis
Make indicators clear
- Power BI reporting and management-control dashboards
- Central data cube and shared data model
- Automated refresh and agency-wide reporting
- User onboarding and support with the new views
- SQL and data exploration
- Analysis by business segment
- Margins and conversion indicators
From need to readout
- Data & AI project coordination
- Business problem analysis
- Collaboration with business and technical teams
- Results communication for decisions