Data Science Intern - Paris, Mentorship, Remote Days

Mirakl; Paris; Hybrid; EUR 10,000 - 15,000; Full time

Benefits offered by this job

Breakfasts & wellness

Mentoring from senior experts

Remote work days

Gymlib well-being

Office near Eiffel Tower

Transit covered

Job summary

Mirakl in Paris is seeking a Data Science Intern for January 2027 to join our Data Science team of ~70 data professionals. You will leverage rich marketplace data to grow customer revenue, optimize operations, and ensure transaction safety.

Responsibilities include analyzing and preparing data, prototyping and iterating ML algorithms, collaborating with Data Engineers to move models to production, building dashboards, and presenting findings weekly to the team.

Qualifications

• Current final-year Master's student in Computer Science or related field.
• Familiarity with data preparation techniques and feature engineering.
• Understanding of statistics, machine learning, and deep learning concepts.
• Interest in industrialization and scaling of data science models beyond experiments.

Responsibilities

• Analyze and prepare data to ensure quality for algorithms.
• Prototype and iterate on algorithms with mentor support.
• Collaborate with Data Engineers to deploy models to production.
• Build dashboards to monitor model performance in production.
• Present findings weekly in team meetings.

Skills

Python

SQL

Communication

Autonomy

Teamwork

Education

Master's in Computer Science (Bac+5)

Tools

AWS

Google Cloud

Azure

TensorFlow

Keras

PyTorch

Databricks

Spark

Airflow

Delta Lake

Streamlit

pydantic

Pandas

scikit-learn

Job description

Mirakl in Paris is seeking a Data Science Intern for January 2027 to join our Data Science team of ~70 data professionals. You will leverage rich marketplace data to grow customer revenue, optimize operations, and ensure transaction safety.

Responsibilities include analyzing and preparing data, prototyping and iterating ML algorithms, collaborating with Data Engineers to move models to production, building dashboards, and presenting findings weekly to the team.