Freelancer profile translated to English.

Description

Data Analyst with 4 years of experience, I help companies transform their data into reliable and actionable analysis tools to improve operational performance, business steering, and decision-making.

I primarily use SQL, Python, Power BI, BigQuery, and DBT to structure, analyze, and leverage data from various environments: IoT, operations, retail, product performance, or business reporting.

Some achievements:

• Automation of analytical workflows reducing diagnostics from several days to a few minutes;

• Creation of Power BI dashboards and KPIs for performance monitoring and operational steering;

• Analysis of complex data to identify trends, anomalies, and improvement opportunities;

• Design of data models facilitating reporting and analysis for business teams.

I particularly appreciate environments where data has a concrete impact on operations, products, user experience, or business performance.

Skills & areas of expertise:

• Analytics & BI

Power BI dashboard creation, KPI definition, automated reporting, performance analysis, and decision support.

• Data transformation & pipelines

Data structuring and transformation with SQL, Python, DBT, and BigQuery, automation of analytical workflows, and preparation of reliable datasets for analysis.

• Advanced analysis & data quality

Anomaly detection, trend analysis, root cause identification, improvement of data quality and reliability.

• Operational & IoT data

Telemetry analysis, field data, and connected systems for operational monitoring, equipment reliability, and performance.

You can find my GitHub portfolio here: github.com/memed69009

Industry field of expertise

Languages

French
Native or bilingual
English
Fluent
Spanish
Conversational

Workplace preferences

Can work on-site

Lyon (up to 50km), Marseille (up to 50km), Lille (up to 20km)

Otodata
IoT Data Analyst
TECH
May 2025 - Today (1 year and 3 months)
Montréal, Canada
Working with telemetry data from sensors and embedded systems in a large-scale IoT environment.

Advanced Analysis & Diagnostics

Developing analysis tools in Python and SQL to automate field data diagnostics
Identifying complex anomalies and technical correlations (sensors, firmware, electrical instability)
Analyzing intermittent error sequences and determining root causes

Data Engineering & Pipelines

Designing scalable data processing pipelines to handle large volumes of unstructured data
Structuring and normalizing data from multiple IoT sources
Implementing data validation and reliability processes

Visualization & Steering

Creating and maintaining Power BI dashboards for operational KPI monitoring
Producing actionable analyses for technical and business teams

Machine Learning

Developing anomaly detection and prediction models (clustering, regression)
Applying statistical methods (hypothesis testing, cross-validation)

Impact

Reducing diagnostic time from several days to a few minutes through automation
Improving proactive anomaly detection on field devices
Ensuring reliability of data used for decision-making

Stack: Python, SQL, Spark, Power BI, Azure, Databricks, Machine Learning, IoT
SQL Server IoT Microsoft Power BI Python (Programming Language) Time Series
VOLVO group
Data Analyst Engineer
AUTOMOBILE
April 2022 - November 2024 (2 years and 7 months)
Lyon, France
Analysis of vehicle and embedded systems data in an industrial context (field tests, performance, diagnostics).

Data Analysis & Quality

Collecting, cleaning, and analyzing data from vehicles (field tests, endurance, performance)
Validating data quality and compliance according to client requirements
Automating the generation of analysis reports

Diagnostics & Continuous Improvement

Analyzing technical data to identify anomalies and performance drifts
Formulating recommendations to improve system reliability
Optimizing data analysis processes

Visualization & Reporting

Developing Power BI dashboards for key performance indicator (KPI) monitoring
Creating advanced visualizations (Python – Matplotlib, Seaborn)

Product Collaboration

Collaborating with hardware, firmware, and customer support teams
Integrating diagnostic tools into product interfaces
Writing clear diagnostic messages for end-users

Impact

Improving the reliability of vehicle data analyses
Reducing processing and analysis time through automation
Better understanding of system behaviors through data analysis

Stack: Python, SQL, Power BI, PySpark, Databricks, Data Analysis, IoT, Embedded Systems, Statistics, Machine Learning
Time Series PowerBI PostgreSQL Machine Learning Streamlit
Airbus Helicopters
Sensor Anomaly Detection
AVIATION AND AEROSPACE
October 2021 - November 2021 (1 month)
Lyon, France
Objective: Automatically detect abnormal behavior of helicopter vibration sensors
• Data analysis, cleaning, and preparation:
Þ Research and processing of outliers, missing values, and duplicate data
Þ Frequency resampling
• Data visualization using Matplotlib and Seaborn: Signal distribution, ACF and PACF visualization
• Data preprocessing:
Þ Dimensionality reduction
Þ Feature engineering: feature calculation
• Implementation of anomaly detection algorithms:
Þ Ensemble method: Feature Bagging
Þ Local Outlier Factor
Python Statistics Anomaly Detection Time Series PCA