Internship - Data Scientist - Suppliers' - Clamart, France - Schlumberger

Schlumberger

Entreprise vérifiée

Clamart, France

il y a 3 semaines

Posté par:

Sophie Dupont

beBee Recruiter

StageSHIP

Description

Internship - Data Science - 2

Suppliers' similarity and segmentation models:
unsupervised approaches

About SLB
We are a global technology company, driving energy innovation for a balanced planet.

At SLB we create amazing technology that unlocks access to energy for the benefit of all. That is our purpose. As innovators, that's been our mission for 100 years. We are facing the world's greatest balancing act
- how to simultaneously reduce emissions and meet the world's growing energy demands. We're working on that answer. Every day, a step closer.

Our collective future depends on decarbonizing the fossil fuel industry, while innovating a new energy landscape. It's what drives us. Ensuring progress for people and the planet, on the journey to net zero and beyond. For a balanced planet.

Our Purpose
Together, we create amazing technology that unlocks access to energy for the benefit of all.

Job title
Internship - Suppliers' similarity and segmentation models: unsupervised approaches (6 months)

Location
Clamart, France

Description
Supplier similarities and segmentation are two important concepts that can be used to manage our supply chain more effectively. Supplier similarities refer to the ways in which different suppliers are similar to one another.

These similarities can include factors such as the type of products or services they offer, their location, their size, and their overall business model.

Identifying similarities among suppliers can help us streamline its sourcing processes, standardize its procurement procedures, and negotiate better terms with suppliers.

Segmentation, on the other hand, refers to the process of dividing suppliers into different groups or categories based on certain criteria.

This can help us better understand the different types of suppliers it works with and tailor its approach to managing them accordingly.

For example, we can segment our suppliers based on factors such as the criticality of their products or services, the level of risk they pose to the business, or the level of collaboration they require.

This internship involves working on a project using unsupervised machine learning techniques to extract insights from data without the use of labeled examples.

It includes clustering, dimensionality reduction, and anomaly detection.

You will work on:

Preprocessing and cleaning data for analysis.
Implementing and developing unsupervised learning algorithms.
Evaluating the performance of different techniques on different datasets.
Using visualization tools to explore and understand the results.

Deliverables
code (python), numerical models, Internship presentation and report.

Target Disciplines and Special Skills

Penultimate or final year of MSc in Data Science or similar Engineering degree
Oral and written communication skills in English
Knowledge in applied mathematics, probability & statistics, manifold learning.
Proficiency in Python programming and algorithmic development.
Experience in scikitlearn/scipy/numpy/pandas/spark.
Good motivation, autonomy, teamwork, and ingenuity
Knowledge in geology is a plus

SLB is an equal employment opportunity employer.

Qualified applicants are considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or other characteristics protected by law.