Payplug

- Compensation plans
- Process automation
- Sales performance
- ABM orchestration
- Tool monitoring

Lead Revenue Operations

Hyperline

The mission consisted of four projects:

1. Tools rationalization
2. Tracking & Reporting
2. New pipeline creation + Migration – on Attio.
3. Lead routing & Lead ingestion | Lead sources → Make → Attio

Revenue Operations

Alegria.group

My mission is to generate revenue through all pipeline stages to:

1. Alegria.solutions - NoCode & IA Agency (B2B)
2. Alegria.academy - School + Online Training (B2C & B2B)

▸ I manage the Growth, SDR and Ops teams.

▸ I produce all the Marketing and Sales dashboards.

My data stack: BigQuery, Stitch, Airflow, n8n, Make, Whaly

Head of Growth & Ops

Working for three different business models with their own growth strategy:

1. Alegria.solutions | NoCode & IA Agency
2. Alegria.academy | School & Online Training
3. Alegria.network | First Worldwild NoCode Makers Community

• Growth Strategy
• Revenue Ops (Marketing & Sales Ops)
• Growth Inbound & Outbound

Growth Lead

Université de Rennes

Professional Teacher

Delta Business School

Scalezia.co

Advisor | Growth & RevOps

Oreegami

Paris School of Sports

Klox

• Growth Ops (CRM, Enablement, Dashboarding)
• Lead Gen (Inbound / Outbound)
• Sales Automation
• Marketing Automation

Head of Growth

• Co-leading a team of media traders
• Deliver programmatic and social media campaigns for our key accounts
• Deal with ad networks/publishers
• Data projects coordination:
     - DMP/CDP Integration,
     - Reporting & Analyses,
     - Good understanding of the subject in intern.

Senior Media Trader - Head of Data

• Setting up, monitoring and optimizing programmatic campaign through different DSP
DSP: AppNexus, Mediasmart, Adobe Ads (formerly TubeMogul), Amazon Ads, Audiomatic.
SMO: Google Ads, Facebook/Instagram, Snapchat, LinkedIn, Twitter, Reddit, Pinterest
• Reporting and analyzing data for optimizations purposes
• Continually enhancing programmatic strategies from our learnings

Media Trader

Ubisoft

• Setup, monitoring and optimizing media campaigns (Mainly on AdWords)
• Ad Trafficking (DCM)
• Reporting & Analyses via different platforms: AdWords, DBM, DCM, AdGear, Facebook)
• Support & Project Coordination for the Media Team.

Assistant Media Manager

Head of Growth & Ops @Alegria.group | Growth Teacher

I'm passionate, and I'm writing about Growth, Revenue Ops, Nocode and IA. Tech News in general.


🛠 Hard Skills

· Content Marketing, Copywriting & SEO
· Process Automation & RevOps
· Advertising (Online & Offline)
· Lead Generation
· Landing Page
· Data Analysis
· CRM


🛠 My Preferred Nocode Tools

· n8n
· Make
· Airtable
· Webflow
· Whaly (BI)

Hugo Cornu

ReLU

🏢 Companies : CPAM, SFR, Diot Siaci, Midas

👉 Project: 
Integration of an LLM into an application for a specific business use case.

🎯 Context:
Within the product engineering teams at SFR and the development teams at CPAM, Diot Siaci and Midas, I trained these teams in using GPT via the API, prompt-engineering techniques and integrating both into FastAPI, Flask or Streamlit applications.

✅ Missions:
➤ Challenge business requirements and define the scope through a reusable technical specification
➤ Integrate prompt-engineering techniques (Chain-of-Thought, zero-shot, one-shot and few-shot learning)
➤ Build a simple chatbot application using FastAPI or Flask
➤ Develop a full chatbot application with prompt and response storage via Streamlit
➤ Define the diﬀerences between Machine Learning, Deep Learning and LLMs
➤ Containerise the application using Docker

Data Scientist

🏢 Company : ENGIE (1 month) 

👉 Project: 
Creation of a prompt engineering repository to automate content creation.

🎯 Context:
For ENGIE’s Legal Research & Development team, I created a knowledge base to enable the business unit to automate content creation, retrieval and verification. I also trained the team to use LLMs and techniques such as zero-shot, one-shot and few-shot learning.

✅ Missions:
➤ Define what AI is and where LLMs sit within the field of AI
➤ Explain how to maintain critical thinking when using an LLM
➤ Advise on how to use LLMs as a legal researcher
➤ Master techniques such as Chain-of-Thought, zero-shot, one-shot and few-shot learning
➤ Demonstrate and embed best technical practices within the team
➤ Explain how to detect biases in outputs and audit a prompt from its formulation to the responses obtained

AI Engineer

🏢 Company : ALLEGO (3 months) 

👉 Project: 
Created a data pipeline following a Kappa architecture.

🎯 Context:
For Allego, a renewable energy company and European leader in charging networks, I designed a data pipeline using a Kappa architecture to automate the quarterly charging-data submissions that were previously sent manually by the business to a new government service (Qualicharge) via a batch API under the OCPI protocol. The charging dataset comprised both static and dynamic elements: the dynamic data consisted of charging sessions, while the static data detailed charger locations and their charging capacities.

✅ Missions:
➤ Defined and implemented a Kappa architecture on Azure Databricks
➤ Read data from continuously ingested Delta Lake tables
➤ Verified outgoing fields against a reference table
➤ Identified and corrected erroneous fields using validated data
➤ Sent the cleansed data to the government API via HTTP POST requests
➤ Managed HTTP POST sessions with secure authentication
➤ Implemented a logging and alerting system to monitor the data transmissions

Data Engineer

Jedha Bootcamp

🎯 I teach for the Data Science and Data Analysis bootcamps.

✅  Subject taught:  
➤ Business analytics 
➤ Product Management 
➤ Product Analysis 
➤ Looker Studio 
➤ APIs
➤ GoogleSheet
➤ Financial Analysis
➤ Amplitude
➤ Google Analytics
➤ Google Tag Manager
➤ Machine learning (unsupervised and supervised learning)
➤ Python (Pandas, NumPy)

Data Science and Analytics Teacher

Le Wagon

🎯 I teach for the skill courses and Data Science bootcamps at Le Wagon, as well as for the bootcamp preparation programs.

✅  Subject taught: 
➤ SQL 
➤ BigQuery 
➤ Cloud Function 
➤ Cloud Scheduler
➤ DBT
➤ Git/GitHub 
➤ ETL
➤ n8n 
➤ APIs 
➤ Python (Pandas, NumPy)
➤ Machine learning (unsupervised and supervised learning)
➤ Deep learning (CNN, RNN)

Data Science & Analytics Teacher

Better&Stronger

👉 Project 1:
Classification of visitors to a hotel booking website in order to analyse potential purchasers.

🎯 Context: 
Traﬃc data from a hotel group’s website is collected and used to analyse user behaviour and optimise the site journey. For advertising purposes, the Marketing team leverages these data to build targeted ad audiences and promote the hotel’s oﬀerings. Classifying users therefore helps to understand distinct user segments and the common behaviours that drive them to purchase.


✅ Missions: 
➤ Audited the existing classification model to determine why it failed to meet marketing requirements
➤ Analysed the dataset to demonstrate overfitting in the current model
➤ Advised the Data team on the scarcity of positive labels (58 million rows, only 0.00418% positive)
➤ Identified correlations between user events to isolate the most relevant features
➤ Built the first draft of the data pipeline, including data cleaning and splitting into training, test and production sets
➤ Retrained the existing Boosted Tree Classifier; trained Logistic Regression and Random Forest models
➤ Calculated feature importances and deployed the Random Forest model into production
➤ Presented findings and delivered workshops to both the Marketing and Data teams

👉 Project 2:
Categorisation of users’ purchase probabilities on the website using a clustering model.

🎯 Context: 
Following on from the user classification project, the Marketing team wishes to categorise the probability segments as follows: very low purchase probability, low purchase probability, moderate purchase probability and high purchase probability. These segments will be used by the Marketing team to create personalised audiences to enhance the performance of their online advertising.

✅ Missions: 
➤ Applied arbitrary marketing criteria to define audience segments
➤ Analysed the distribution of model-generated probabilities
➤ Employed a clustering model on the probability scores
➤ Delivered the resulting audience segments into a dedicated table
➤ Presented the audiences and accompanying analyses to the Marketing team

Interencheres

👉 Project 1:
Creation and optimisation of data pipelines using SQLMesh and Metabase.

🎯 Context: 
Within Interencheres’ IT/Data team, I was hired to drive a data-driven vision and establish best data practices across the Data, Marketing and Sales teams. One of my primary projects was to optimise the loading time of Metabase dashboards by improving the underlying data pipeline.

✅ Missions: 
➤ Audited the data team’s practices to identify bottlenecks causing latency in Metabase
➤ Conducted preliminary statistical analyses (distributions, average MRR, average revenue per user, purchase frequency, etc.)
➤ Reviewed the existing data pipeline, outlining its pros and cons and recommending enhancements
➤ Developed an initial pipeline example based on the medallion architecture, driven by Marketing requirements
➤ Achieved a tenfold reduction in query response times and dashboard load times in Metabase
➤ Managed the team through a SQLMesh proof of concept to spearhead internal technical change
➤ Delivered the first Marketing dashboards covering product usage and revenue by user segment
➤ Trained the IT/Data team on data best practices
➤ Provided training to the Marketing team on how to leverage the Data team’s capabilities

👉 Project 2:
User scoring with the aim of creating user segments and improving marketing performance.

🎯 Context: 
At the request of the Marketing team, I analysed user behaviour to deliver initial business and technical insights aimed at understanding who our users are. Having never previously analysed user data, the Marketing team relied on these results to refine their campaigns and guide their decision-making, as well as to present the findings to the Executive Committee.

✅ Missions: 
➤ Collected and structured data organised into user microservices via a medallion architecture and columnar views
➤ Exported user data using Python
➤ Implemented RFM scoring on user data
➤ Categorised users based on Recency, Frequency and Monetary scoring
➤ Classified users by purchase recency, frequency and monetary value
➤ Defined user retention metrics by purchase recency and frequency
➤ Segmented users into clusters based on monetary spend
➤ Calculated users’ Lifetime Value according to their frequency and financial expenditure
➤ Analysed and visualised purchase trends through time-series analyses
➤ Documented each analysis for the Marketing team
➤ Documented the notebook to be reusable in a single “Run all” and understandable by both the Data and Marketing teams

👉 Project 3:
Automating the forecasting of annual revenue.

🎯 Context: 
The Sales team had been accustomed to producing revenue forecasts manually via Google Sheets, while collecting past years’ data by hand. Upon learning this, I organised a meeting to assess the tools at our disposal and ultimately proposed automating the forecasting process.

✅ Missions: 
➤ Collected and cleaned sales data organised into microservices
➤ Analysed erroneous sales records and provided them to the IT/Data team for error investigation
➤ Developed an initial forecasting model using ordinary least squares regression
➤ Calculated the deviations between predicted and actual values
➤ Built a second forecasting model using exponential smoothing
➤ Discovered further corrupted data and, due to excessive data corruption in the production database (the project was cancelled)
➤ Instead, created a dashboard displaying:
	➢ Weekly revenue (Sunday to Sunday)
	➢ Diﬀerence between current week’s revenue and the previous week’s
	➢ Diﬀerence between forecasted revenue and actual revenue for the current month
➤ Percentage increase or decrease in revenue versus the same period the previous year

beta.gouv.fr

👉 Project 1: Audit and recommandation on data management and product ownership.

🎯 Context: 
For La Plateforme de l’Inclusion, an incubator of the Ministry of Labor, I conducted an audit based on the calculations and dashboards already created by the IT team of a startup called Immersion Facilitée. The ultimate goal was to highlight inconsistencies and implement best practices in data and product management.

✅ Missions:
➤ Audited statistical impact calculations, including cost price, supply and demand, registration numbers, usage rates and NPS
➤ Used TypeScript to create database migrations and modify the data schema
➤ Presented organisational recommendations on data management and product ownership
➤ Calculated and presented initial OKRs and KPIs for each business team

👉 Project 2: Calculate the impact of the startup (financial soundness).

🎯 Context: 
Still for the startup Immersion Facilitée, incubated at La Plateforme de l’Inclusion (an incubator of the Ministry of Labor), I carried out statistical analyses to justify the bi-annual funding allocated by DINUM and France Travail. The calculations were based on the financial health and the impact achieved by Immersion Facilitée, with the main indicator being the time saved by users.

✅ Missions:
➤ Calculated 12-month moving averages
➤ Analysed and presented company retention metrics (registered vs non-registered)
➤ Created impact indicators and presented them alongside the OKRs on the website

👉 Project 3: Supply and demand studies 

🎯 Context: 
Study of supply and demand on Immersion Facilitée, a startup incubated at the Ministry of Labor’s incubator, La Plateforme de l’Inclusion. The supply depended on the number of job positions offered directly on the platform by companies, compared to the job searches carried out by future interns. 

✅ Missions:
➤ Supported the growth-hacking coach in implementing business and data initiatives
➤ Conducted geographical analyses intersecting immersion oﬀers posted (thousands per week) with candidate searches (millions per week)
➤ Presented findings for supply-and-demand studies between oﬀers and searches
➤ Developed a Streamlit application for the business teams to analyse supply and demand

🏢 Company : SCIKIT-LEARN (1 week) 

👉 Project: 
Creation of the first oﬃcial Scikit-learn certification by Scikit-learn.

🎯 Context:
During a sprint within the Scikit-learn team, we aimed to develop the first version of their oﬃcial certification across three levels—Associate, Professional and Expert. I had the opportunity to create the technical content for the Associate and Professional levels, and the certification is now available on their website.

✅ Missions:
➤ Write theoretical machine-learning questions for the Associate and Professional levels
➤ Develop Python and machine-learning exercises using scikit-learn to assess certification candidates
➤ Evaluate datasets and their licences for inclusion in the certification programme
➤ Brainstorm compelling and relevant machine-learning use cases to include in the certification syllabus

IPSSI

🎯  As a professor of data science for the Master in Marketing & Data program, I teach how data science can assist marketing.

✅ Subjects taught:
➤ Python (Pandas, Numpy, OOP)
➤ Web Scraping (Python, Scrapy)
➤ Data Mining (Cleaning and interpreting collected texts)
➤ Data Analysis (Analyzing the retrieved data)
➤ ELT, ETL (Processes for creating data pipelines)
➤ NLP (Transforming cleaned texts into useful and accessible information)
➤ Classification (Creating a classification algorithm for texts by topics)

Marketing Data Scientist Teacher

HETIC

🎯 As a professor of Data Science for first-year Master’s students in the Data Science & Big Data program, I teach the foundations of programming, statistics, and machine learning.

✅ Subjects taught:
➤ Python (OOP, Pandas)
➤ Exploratory Data Analysis (EDA)
➤ Data visualization (Seaborn, Plotly Express, Plotly Graph Objects)
➤ Statistics and Probability
➤ Supervised Machine Learning

Data Science Teacher

Sup de Vinci - école d'informatique

🎯 As a professor supporting Master’s students during a Big Data Hackathon, I guided them in applying data science and engineering skills to create a complete project.

✅ Subjects taught and supported
➤ Project management and team organization
➤ Python programming for data processing and analysis
➤ React development for building project interfaces
➤ Grafana for data visualization and monitoring

Big Data Mentor

I help companies transform their data into actionable insights and scalable solutions.

My work focuses on two areas:
➢ 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 & 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: turning raw data into predictive models and decision-making tools.
➢ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 & 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: building robust pipelines and automating processes to save time and boost performance.

I also teach data science and machine learning, because empowering teams with the right skills creates autonomy and long-term impact.

👉 If you want to leverage your data to drive growth, optimise processes, and automate workflows, let’s talk.

𝐊𝐞𝐲 𝐬𝐤𝐢𝐥𝐥𝐬

📊 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 & 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
➢ Exploratory analysis & data visualisation
➢ Data collection & pipelines (APIs, scraping, ETL/ELT)
➢ Predictive modelling (supervised & unsupervised ML)
➢ Application development (Streamlit, FastAPI)

⚙️ 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 & 𝐎𝐩𝐭𝐢𝐦𝐢𝐬𝐚𝐭𝐢𝐨𝐧
➢ Growth, marketing & sales processes
➢ APIs (REST, GraphQL)
➢ ETL/ELT (Python, Cloud Functions)
➢ Low-code tools (n8n, Metabase)
➢ Cloud solutions (GCP: BigQuery, Cloud Functions, Pub/Sub, Bucket, Scheduler)

🛠 𝐓𝐨𝐨𝐥𝐬 & 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬
Scikit-learn | Python | SQL | GCP | Metabase | Streamlit | FastAPI | APIs | Statistics

📩 Feel free to reach out if you want to explore how data can create value for your business.

taplio

The Battle

Hugo Cornu

Tuka BADE

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