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Malt Strategy
Mélanie JanvresseMJ

Mélanie Janvresse

Data analyst

€125/day
Berlin, DE
0-2 years
Python
R
Machine learning
Microsoft Power BI
Data analysis

Average response time: 1 hour

About Mélanie

As a Data Analyst, I turn fragmented, multi-source data into trustworthy insights and impactful dashboards. Clients come to me when they need someone who can not only analyse data, but organise it, automate it, and make it usable at scale.

My background in Cognitive Science and statistical modelling—combined with hands-on experience with Python, R, SQL, Power BI, and Google Apps Script—allows me to navigate both business-oriented and research-level problems with precision.

I can help you:
- Rebuild and clean historical datasets from spreadsheets, platforms, and APIs
- Automate your reporting workflows to eliminate repetitive manual work
- Analyse costs, revenues, performance, or customer behaviour
- Build dashboards with clear KPIs, beautiful visuals, and intuitive navigation
- Run statistical or machine learning analyses for deeper insights or predictions

My added value:
- Strong ability to handle very messy data and reconstruct reliable datasets
- Clean, maintainable automated workflows (Google Apps Script, Power Query, Python)
- Evidence-based decision support (financial insights, market trends, statistical validation)
- Clear communication and structured documentation
- A rigorous, research-informed approach to data quality and interpretation

Project examples:
- Reconstructed 10+ years of financial data from scattered sources for a Berlin cultural organisation
- Built automated workflows and a Power BI dashboard for strategic budgeting
- Scraped and analysed 3,800+ real estate listings to generate actionable market insights
- Processed and modelled thousands of physiological data points for a university research lab

  • French

    Native or bilingual

  • English

    Native or bilingual

  • German

    Conversational

Can work on-site
Berlin (up to 30km)

Experience

  • La Ménagerie e. V.
    Data Analyst
    April 2025 - Today (1 year and 2 months)
    Berlin, Germany
    - Reconstructing, cleaning, and structuring ten years of fragmented financial data from inconsistent sources (Facebook events, ticketing platforms, and Google Sheets) using Google Sheets (IMPORTRANGE, Apps Script).
    - Identifying automation opportunities and building a custom script to streamline data processes, significantly saving time and reducing manual effort and errors.
    - Analyzing historical costs and revenues with Google sheets to identify the most profitable types of performances and projects.
    - Creating an interactive Power BI dashboard to visualize key financial insights and improve future budget planning.
    Microsoft Power BI Google Sheets
  • Self-Initiated Project
    Berlin Rental Housing Market Analysis
    February 2025 - March 2025 (1 month)
    Link to GitHub Project: https://github.com/Hadelockeuse/Rental_Housing_in_Berlin

    - Collected, cleaned, and structured data from 3,808 rental listings (final dataset: 2,294) using web scraping and preprocessing with Python (Selenium, BeautifulSoup, Requests).
    - Reshaped the dataset by unpivoting categorical columns (floor covering, heating type, heating system) in Power Query, enabling the comparison of median rents across subcategories (e.g., parquet vs. tiles) in Power BI dashboards.
    - Built interactive dashboards with DAX formulas (dynamic titles, axis scaling, and color-coding) and improved usability using slicers and bookmarks.
    - Provided actionable insights, such as advising aspiring tenants to favor subdistricts like Oberschoneweide and Heinersdorf, opt for properties with floor heating over central heating, and be ready for immediate move-in (70% of listings), to increase their chances of securing more affordable flats.
    Microsoft Power BI web scrapping Python Data visualization
  • Academic Project
    Income Classification with Machine Learning
    April 2023 - September 2023 (5 months)
    Link to GitHub Project: https://github.com/Hadelockeuse/income_classification

    - Developed Random Forest and SVM models to classify income levels based on demographic and employment data, practicing data storytelling through a visual report and a stakeholder-oriented documentation.
    - Achieved 88.6% accuracy and 91.1% ROC-AUC, demonstrating strong predictive performance despite class imbalance ( 75% earn $50K).
    - Applied feature selection, hyperparameter tuning, and cross-validation to enhance model robustness.
    Machine learning Python
Check out Mélanie's experience

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Education

  • Master's degree in Cognitive Science – Embodied Cognition
    University of Potsdam
    2025
    Master's degree in Cognitive Science – Embodied Cognition
  • Bachelor's degree in Linguistics
    University of Nantes
    2020
    Bachelor's degree in Linguistics

Certifications

Skill set

Matlab
Data visualization
SQL
web scrapping

Categories