Imane El Missaoui

Based in Île-de-France

Available for CDI opportunities

Data Engineer · Data Analyst

From raw data to human insight.

My kind of work ↓

I design reliable data flows and clear analytics—especially where complex healthcare data needs to become useful evidence.

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97.3%reliable result

Evidence someone can use.

Scroll to follow the data
01

Models / selected case files

Not project cards.
Data stories.

Each case is traced from its real source to the outcome it made possible.

CASE / 01 Making 10,000+ medical images traceable and analysis-ready. ● documented

Endoscopic image pipeline

Image batches / QC
10K+images organised
Source

Capsule endoscopy images across separated dev, test and production environments.

Challenge

Turn a large, sensitive image collection into a dependable flow for experimentation and evaluation.

Pipeline
  • MongoDB
  • Python
  • Airflow
  • Quality checks
Result

A structured dataset and reproducible workflow supporting 4-class evaluation with 97.3% test accuracy.

source → structure → evidence Trace the project
CASE / 02 Turning high-frequency signals into indicators people can read. ● documented

Physiological signal stream

Live signal / 500 Hz
500 Hzlive signal input
Source

Continuous 500 Hz PPG, ECG and temperature signals.

Challenge

Filter noise, calculate meaningful indicators and communicate them without hiding signal quality.

Pipeline
  • Signal filtering
  • HeartPy
  • Airflow
  • Streamlit
Result

A real-time monitoring flow for heart rate, breathing rate and HRV with an interactive dashboard.

source → structure → evidence Private work · details on request
CASE / 03 Preparing complex neuroimaging for reproducible research. ● documented

MRI cohort analytics

Cohort / 3D volumes
2K+MRI scans prepared
Source

A cohort of more than 2,000 structural 3D MRI scans.

Challenge

Standardise imaging inputs and explore topological biomarkers for Parkinson’s stratification.

Pipeline
  • 3D MRI
  • Preprocessing
  • TDA
  • Cohort analysis
Result

A reproducible research workflow connecting imaging preparation to interpretable cohort analysis.

source → structure → evidence Trace the project
CASE / 04 Structuring public clinical-trial data into a reproducible healthcare pipeline. ● in progress

Oncology clinical trials pipeline

API / Bronze layer
JSONB raw clinical-trial storage
Source

Public lymphoma and oncology study records retrieved from the ClinicalTrials.gov API v2.

Challenge

Preserve deeply nested and variable healthcare data while preparing reliable structures for analysis.

Pipeline
  • Python
  • REST API
  • Docker
  • PostgreSQL
Current result

A documented source profile and Dockerized PostgreSQL Bronze layer designed to preserve complete trial records as JSONB.

API → raw JSON → PostgreSQL Bronze Trace the project
02

Lineage / how I arrived here

Every role added
a new layer.

My career is not a list of positions. It is a growing data lineage—from scientific questions to reliable systems and decisions.

01
2025 — 2026

PET / CT · Multicentre

Medical Imaging Data Scientist

Built a reproducible radiomics workflow across DICOM, NIfTI, segmentation masks and feature tables for lymphoma research.
02
2023 — 2024

LIP6 · Sorbonne Université

Data Scientist & Imaging apprentice

Worked across medical-image databases, automated data processing and real-time physiological monitoring.
03
2023

Lille Neuroscience & Cognition

Neurodata research intern

Prepared and analysed a cohort of 2,000+ MRI scans for Parkinson’s disease stratification research.
04
Before data

Physics · Healthcare engineering

The foundation

A scientific path that shaped how I question sources, test assumptions and communicate evidence.
03

System / tools in context

A working ecosystem,
not a keyword cloud.

I choose tools by the job they need to do, and connect them into a flow that stays understandable.

01 · Ingest

  • Python
  • SQL
  • DICOM
  • REST data
  • MongoDB

02 · Orchestrate

  • Airflow
  • Docker
  • Git
  • Linux
  • Quality checks

03 · Transform

  • PostgreSQL
  • Pandas
  • R
  • SimpleITK
  • PyRadiomics

04 · Communicate

  • Power BI
  • Streamlit
  • R Shiny
  • Statistics
04

Human / behind the pipeline

Rigour from science.
Curiosity for people.

I care about the quiet work that makes data trustworthy—and the final mile that makes it understandable.

My path started with physics, continued through healthcare engineering, and became a specialisation in health data. I am now moving toward Data Engineering and Data Analytics roles where I can make information more reliable, accessible and useful.

Education record Master’s in Data Science for Health

ILIS · Université de Lille · 2024

05

Output / let’s connect

Have complex data?
Let’s make it useful.

Open to Data Engineer and Data Analyst opportunities in France, with a particular interest in healthcare and meaningful data environments.

LinkedIn ↗ GitHub ↗