Data Science Intern (6 Months) - ML Operations: drift detection, prediction confidence, and shadow deployment
We are Schlumberger, the leading provider of technology and services to the energy industry. Operating in over 120 countries, our people provide leading digital solutions and deploy ground-breaking technologies to unlock cleaner, safer access to energy for every community—including those we live and work in. 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.
For reliable operations of ML models we rely on prediction confidence and drift detection for serving models and shadow deployment for models in staging. Both present challenges from an algorithmic and performance point of view about which this internship will be.
Essential Responsibilities and Duties:
You will work on building critical ML operation components on state-of-the-art infrastructure and software stack. These components ensure reliable operation of models in production and give actionable insight on the performance of proposed model changes. Specifically, work will include deployment pipelines, serving mechanisms of both the production and shadow model, improving the definition of performance metrics and design of feedback mechanisms.
Penultimate or final year student, studying towards Bachelors or Masters in Computer Science, Mathematics or related field.
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.