Data Science, Computer Science Intern – Create a predictive model to anticipate MFG system level test results (Assemblies) using unit level test data (Circuit boards, nuclear detectors) (6 months) | Schlumberger

Job Details

Data Science, Computer Science Intern – Create a predictive model to anticipate MFG system level test results (Assemblies) using unit level test data (Circuit boards, nuclear detectors) (6 months)

Clamart - France

Job title:

Data Science, Computer Science Intern – Create a predictive model to anticipate MFG system level test results (Assemblies) using unit level test data (Circuit boards, nuclear detectors) (6 months)

 

Location:

Clamart, France

 

About Schlumberger:

We are Schlumberger, the leading provider of technology and services to the energy industry. Throughout much of the oil and gas lifecycle in over 120 countries; we design, develop, and deliver technology and services that transforms how work is done.

We define the boundaries of the industry by unleashing our talented people’s energy. We’re looking for innovators to join our diverse community of colleagues and develop new solutions and push the limits of what’s possible. If you share our passion for discovery and want to find out what you could really do, then here is the place to do it.

 

Job Summary:

The internship will comprise 4 main packages (see list below). Then, depending on the timeline, candidate skillset and exploratory outcomes, the internship's scope could be expanded to work packages 5 and 6. 

 

Essential Responsibilities and Duties:

WP1: Structured and un-structed data parsing to build a store with the historical data sets 

WP2:Visualize and compare data from the Historical Database for all nuclear Tests.  

WP3: Select datasets with high potential for early failure prediction (collaborate with manufacturing and physics subject matter experts)  

WP4: Create Data Analytics predictive model to predict XPET test failures based on detectors & detectors test results. 

WP5: Explore models for prediction of Calibration / NCA  failures based on XPET results. 

WP6: Explore the integration of mechanical parts variabilities and characteristics into the models 

 

Qualification:

  • Master’s degree – 2nd year or final year of Engineering School

 

Competencies:

  • Data mining  
  • Data analytics  
  • Unstructured data processing 
  • Programming (Python)
     

Schlumberger 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.

 

Data Science, Computer Science Intern – Create a predictive model to anticipate MFG system level test results (Assemblies) using unit level test data (Circuit boards, nuclear detectors) (6 months)
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