от компании (организации): Aramco Innovations в городе (населённом пункте): Москва, Россия
в отрасли экономики "Наука, образование" → "Информатика, информационные системы"
с заработной платой: по договоренности
Вакансия № 17773268 добавлена в базу данных сайта Работа в Москве и Московской области (МО, Подмосковье): Пятница, 26 июля 2024 года.
Дата обновления вакансии № 17773268 на сайте Работа в Москве и Московской области (МО, Подмосковье): Воскресенье, 22 сентября 2024 года.
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Требования к опыту работы:
более 6 лет
Тип занятости:
полная занятость
График работы:
полный день
Дополнительные сведения о вакансии: Research Scientist for Reservoir Engineering Data Analytics
Aramco Research Center – Moscow is offering great job opportunities for R&D data scientists specialized in Artificial Intelligence (AI) and Machine Learning (ML) focused on multi-well log analytics and reservoir engineering domains. The position does not separate the roles of research scientist and data scientist and requires to be very flexible and innovative to think and work outside the box. It will focus on translating frontier data analytics and machine learning techniques to research ideas and prototypes to be further deployed to upstream technologies. This will also require for the candidate to be able to grasp end to end multi-well formation evaluation workflow for data processing and data analytics implementation for successful technology development to tackle business problems.
Key functions and responsibilities:
- Develop data analytics and/or machine learning algorithms for multi-well log correlation for reservoir characterization, reservoir modeling and recovery optimization;
- Identify and prepare training datasets from various reservoir characterization data sources;
- Perform feature analysis and labeling;
- Create performance metrics and tracking processes to measure the effectiveness of data science solutions;
- Create a model prototype of data science algorithms for further testing and validation at different scale;
- Train and retrain systems when necessary;
- Extend existing ML libraries and frameworks;
- Perform statistical analysis and fine-tuning using test results;
- Run machine learning tests and experiments;
- Keep abreast of developments in the field.
Key technical skills and competencies:
- Depth of knowledge and experience with Python and its data analytics and machine learning libraries and packages (e.g. scikit-learn, Pandas, TensorFlow, PyTorch, xgboost, catboost, statsmodels). Ability to write robust code in Python;
- Deep knowledge of math, probability, statistics and algorithms;
- Understanding of data structures, data modelling and software architecture;
- Depth of knowledge in machine learning algorithms, such as supervised learning (e.g. Support vector machine, random forest, gradient boosting, deep neural networks (proven background in working with CNN / RNN / LTSM networks)), unsupervised learning (e.g. component analysis clustering, anomaly detection);
- Building and developing robust, reliable, automated machine learning pipelines and design, build and launch new data models in development and production;
- Proficiency in data analytics algorithms for various data types, such as well logging data, seismic and geological data;
- Hand-on experience with visualization and analytics software using commercial software (e.g. TIBCO spotfire) or creating interface prototypes (e.g. in C#);
- Working experience with Petrel and Techlog as well as with Ocean framework for Petrel plugin development is preferred;
- Hand-on experience in data processing, cleaning, filtering, handling and operation with large quantities of structured and unstructured data;
- Good knowledge of feature engineering algorithms specific for oil and gas multi-well logs, formation evaluation and reservoir engineering domains to create parameters that make specific machine learning algorithms be applied, especially for spatial, time lapse or depth related data;
- Ability to develop R&D software prototypes utilizing latest high-performance-computing (HPC) advances, containerization processes and data intensive AI and machine learning methods will be a plus. Familiar with cloud platforms, Linux, Git;
- Good communication skills in English (writing and speaking) are important. Please use profile in English applying for this role.
Откликнуться на эту вакансию: Research Scientist for Reservoir Engineering Data Analytics
Предыдущая вакансия:
Вакансия № 17773255 на должность Менеджер по развитию школы от компании Нек. орг. Международный образовательный центр в городе (населенном пункте) Балашиха