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Machine Learning Software Engineer
Apr 07, 2019
Salary: Negotiable
Experience 0.0-5.0 years
Job Category IT and communications Jobs
Employment Type Full time
Industry Oil & Gas
Skill IT - Software Development
Role Software Developer Jobs ,Software Engineer Jobs
Designation Other
Education Ph.D
No. of vacancies 1
About Company

A tradition of excellence and innovation Schlumberger is the leading provider of oilfield services, focusing on innovative technologies for reservoir characterization, drilling, and production. We're also a leading employer in our sector-with a reputation for hiring the best and the brightest people and keeping them at the top of their game through rewarding career-long development opportunities.

Job Description

A successful Machine Learning Software Engineer is a passionate technologist with impressive programming abilities, able to design and implement solutions for real problems for real people. She/he has a good understanding of the concepts behind Machine Learning, experience with prototyping and has the drive to independently take the journey from problem to working code.

Roles and Responsibilities:

  • Builds enterprise level machine learning architecture within our big data back end, designing algorithms to detect anomalies across large datasets
  • Develop domain-aware pre-processing algorithms, and other methods to increase model generalization
  • Identifies and keeps abreast of novel technical concepts and markets
  • Builds prototypes, products and systems suitable for testing and sets up and runs lab simulations
  • Develops and executes tests for benchmarking purposes and system quality
  • Promotes internal knowledge sharing throughout the organization that may include user training development
  • Actively support industry recognition and positioning of Schlumberger technologies via authoring technical reports, papers, articles and patents

Qualifications and experience:
  • Bachelor / Masters / PhD degree in science or engineering with software experience or education.
  • Applied knowledge of statistical methods and applicable mathematical concepts
  • Applied knowledge of Development Lifecycle Management, DevOps Automation methods and practices, Post-Production Model Monitoring and End-to-End Architecture and the ability to managing workloads over multiple nodes
  • Experience with Python, Numpy, Scikit, Tensorflow, Keras or equivalent
  • Understanding of Apache Hadoop ecosystem internals and development (Hadoop, Map Reduce, HBase, Hive, Pig etc.) is a plus
  • Familiarity with data transformation and integration tools and technologies desired (Spark, data flow, Kafka, Storm, etc.) is a plus
  • Some experience with Linux administration and Linux scripts is desired

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