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kenzi0228/README.md

English · Français

Kenzi LALI

Data Scientist (apprentice) · Data Engineering · Financial Data & BI
Engineering student in Data & AI at ECE Paris · Paris, France

I build data pipelines, analytics and automation on real production data: SAP finance and compliance at Orano, product and client analytics at Moodwork. I care most about one thing: numbers that decision-makers can trust.

Experience

Data Scientist — Apprentice · Moodwork (workplace wellbeing platform) · May 2026 – Sep 2027

  • Built an automated client reporting system (Google Apps Script → Google Slides) fed by BigQuery and Metabase, with batch generation and concurrency safeguards
  • Contribute to Moodwork Insights, a workplace wellbeing analytics product built on anonymised data
  • Co-built an LLM-powered support chatbot on the customer chat (n8n)
  • Design internal AI assistants for business teams and run internal training on building them

Data Scientist (Finance) — Apprentice · Orano · Sep 2024 – Jan 2026

  • Built Power BI data-quality dashboards for financial KPIs
  • Automated Sapin II anti-corruption controls with ACL Analytics and HighBond
  • Processed SAP data for audit, reporting and compliance; detected and fixed inconsistencies in financial systems

Data Engineer — Apprentice · Hector Advisory · Sep 2023 – Sep 2024

  • ETL with Power Query, workflow automation with Power Automate, internal tools with Power Apps, Power BI dashboards

Software Testing Intern · Crédit Agricole CIB · Jul – Aug 2022 — functional testing (Jira), BI analysis (Cognos)

Featured projects

Project What it shows Stack
Scientific citation graph pipeline 16 GB of raw JSON → 5.35M publications in partitioned Parquet → citation & collaboration graphs, centrality, communities, Streamlit explorer Python, Parquet, NetworkX, Streamlit, pytest
Big Data transport × weather pipeline Near real-time Paris metro (IDFM API) + weather ingestion, streaming and batch layers, orchestration Kafka, HDFS, HBase, Hive, MapReduce, Oozie
Beneficiary lifecycle management system Operations automation for beneficiary programmes: intake, eligibility rules, blacklist, fair selection, KPI dashboards Google Apps Script, Google Sheets

Tech stack

Languages — Python, SQL, JavaScript (Apps Script)
Data & ML — pandas, NumPy, scikit-learn, NetworkX
Data platforms — BigQuery, Kafka, Hadoop (HDFS, HBase, Hive), SAP
BI — Power BI, Metabase, Power Query, Tableau
Automation & AI — n8n, LLM APIs, Power Automate, Power Apps
Audit & compliance — ACL Analytics, HighBond
Engineering — Git, pytest, GitHub Actions

Education

  • Engineering degree, Data & AI — ECE Paris, apprenticeship track · 2024 – 2027
  • Exchange semester — Algebra University, Zagreb · May – Jul 2025
  • BSc MIAGE (Information Systems) — Université Paris Nanterre · 2023 – 2024
  • BSc MIASHS (Applied Maths & CS) — Université Paris 1 Panthéon-Sorbonne · 2020 – 2023

Leadership

Director — Cop1 Solidarité Étudiante · 2020 – Jun 2025 — led a multi-level organisation (~20 managers, each running a team) delivering large-scale student aid operations.

Pinned Loading

  1. Data-engineering-citations-and-collaborations-in-science Data-engineering-citations-and-collaborations-in-science Public

    Modular data engineering pipeline to preprocess large-scale academic data and generate citation and collaboration graphs, enabling network analysis (centrality, PageRank, communities) through repro…

    Python 3 1

  2. beneficiary-lifecycle-management-system beneficiary-lifecycle-management-system Public

    A Google Apps Script and Google Sheets based operational system designed to manage beneficiary intake, eligibility validation, annual databases, blacklist governance, historical tracking, dashboard…

    JavaScript

  3. big-data-transport-weather-pipeline big-data-transport-weather-pipeline Public

    Hybrid Big Data pipeline using IDFM real-time transport data and Open-Meteo, with Kafka, HDFS, HBase, Hive, MapReduce and Oozie.

    Python

  4. imdb-genre-classification imdb-genre-classification Public

    This project aims to predict the genre of a movie using numerical features: - Number of votes - Popularity score - Average rating

    Jupyter Notebook

  5. political-spectrum-analyzer political-spectrum-analyzer Public

    Interactive Streamlit app that converts Politiscales-style scores into political profiles, x/y spectrum coordinates, detailed interpretations, and comparisons against a 500-profile reference dataset.

    Python

  6. real-estate-investment-analysis real-estate-investment-analysis Public

    Data pipeline and analytical tool for real estate investment in Île-de-France using DVF, rental and geographic data.

    Jupyter Notebook