Solutions Developer, ML

You will need to relocate to Saudi Arabia for this opportunity.

The Machine Learning Solutions Developer‘s primary role is to focus on the implementation of full-stack scalable systems and data solutions for high-volume, high-velocity data applications and high-performance computing which can accelerate oil and gas R&D projects. This important role works with research scientists across the lab with the primary goal of researching and developing cutting-edge software solutions to complex problems.

Responsibilities

Successfully conceptualize, develop, and test various statistical, AI and machine learning solutions to solve the future challenges.
Research, develop and maintain machine learning and statistical models for business requirements.
Partner with researchers to translate business analytic problems into technical solutions and actionable recommendations across the organization.
Work across the spectrum of statistical modelling, including: supervised, unsupervised, deep learning techniques to apply the right level of solution to the right problem.
Build frameworks leveraging APIs to industrialize AI models across the organization.
Collaborate with data and software engineers to enable deployment of models that will scale across the company’s ecosystem.

Requirements

Master’s degree in Data Science or Computer Science.
Seven years of experience in relevant domains (Energy, Oil & Gas, Chemicals, Natural Resources, Mining & Metals, Utilities or Power) – with hands on experience developing and deploying solutions; at least five of which must have been in statistical software, i.e. Python, R, Octave, TensorFlow, Caffe or Theano.
Experience with big data technologies, such as: DevOps, Scrum, web development frameworks, and one or more programming languages (e.g. C#, Java, and Python).
Ability to utilize open-source and commercial tools and approaches to solve complex problems including information retrieval/extraction, machine learning/deep learning, and networking.
Excellent understanding of front and back-end development.
Experience with software development best practices, including: Agile development; version.
Experience leading or collaborating with teams of data scientists in developing and delivering machine learning models that work in a production setting.
Knowledge of UNIX or Linux environments.

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