Data Science Engineer II

<p><b>Company Overview</b></p>

<p><i>Nabors is a leading provider of advanced technology for the energy industry. With operations in about 20 countries, Nabors has established a global network of people, technology and equipment to deploy solutions that deliver safe, efficient and responsible hydrocarbon production. By leveraging its core competencies, particularly in drilling, engineering, automation, data science and manufacturing, Nabors aims to innovate the future of energy and enable the transition to a lower carbon world.</i></p>

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<p>Nabors is committed to providing equal employment opportunities to all employees and applicants and prohibiting discrimination and harassment of any type without regard to race, religion, age, color, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This applies to all terms and conditions of employment including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. To learn more about our Fair Employment practices, please refer to the Nabors Code of Conduct.</p>

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<p><b>JOB SUMMARY</b></p>

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<p>Develop scalable machine learning components to generalize models to other data sources and product groups</p>

<p>Develop solutions for high volume, low latency applications and can operate in a fast paced, highly collaborative environment.</p>

<p>Data Science Engineer will help develop data science applications using machine learning and statistical techniques, help discover the information hidden in large and complex data sets</p>

<p>Develop processes and tools to monitor model performance over time and analyze data accuracy</p>

<p>Candidate must have experience using a variety of data mining and statistical methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations.</p>

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<p><b>DUTIES AND RESPONSIBILITIES</b></p>

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<ul>
<li>Design and work with Machine learning processes and tools which would maintain and improve the model accuracy in an automated way</li>
<li>Understanding of statistics and hypothesis testing (ex. Confidence Interval, Regressions, Time Series, Clustering, Factor Analysis)</li>
<li>Conduct evaluations of new programs and initiatives to figure out what&
39;s working and what&
39;s not</li>
<li>Use predictive modeling and machine learning algorithms to solve complex problems</li>
<li>Stay abreast of state-of-the-art machine learning technologies; follow code standards and best practices</li>
<li>Develop and maintain forecasting models and tools using frameworks such as TensorFlow</li>
<li>Doing ad-hoc analysis and presenting results in a clear manner</li>
<li>Use Deep Learning frameworks like PyTorch, Tensorflow and MxNet is a plus</li>
<li>Strong written and verbal communication skills</li>
</ul>

<p><b>MINIMUM QUALIFICATIONS/SKILLS</b></p>

<p>&bull; 3-5 years of experience in analytics with at least 2 year of experience in statistical analysis</p>

<p>&bull; Bachelors or master&
39;s degree in quantitative field (Computer Science, Mathematics, Machine Learning, AI, Statistics, or equivalent)</p>

<p>Familiar with the following software/tools:</p>

<ul>
<li>Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R)</li>
<li>Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.</li>
<li>Ability to implement statistical models for forecasting, time series predictions</li>
<li>Knowledge and demonstrable skills in Deep learning architectures and implementation using?Pytorch?or?TensorFlow.</li>
<li>Experience writing production quality software in Python or R to implement predictive models</li>
<li>Experience using web services: REST API, SOAP, WCF etc. is a plus</li>
</ul>

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<p><b>PREFERRED QUALIFICATIONS </b></p>

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<p>Bachelors in Computer Science and a Master&
39;s in Data Science or another quantitative field</p>

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<p><b>PHYSICAL REQUIREMENTS / WORKING CONDITIONS </b></p>

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<p>Travel to rig for domain knowledge and new product testing.</p>

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