Senior Data Scientist- AIQ
ADNOC HQ
Abu Dhabi, Abu Dhabi, AE
منذ 1 يوم
source : Oil & Gas Job Search

AIQ is a new joint venture company between ADNOC and Group 42, which focuses on developing artificial intelligence technologies in the United Arab Emirates.

AIQ develops and commercializes AI products and applications for the oil and gas industry. It aims in providing end-to-end solutions by using its data, cloud and talents to develop AI solutions that seek to reduce costs and generate revenue for its clients.

AIQ embodies an innovative and entrepreneurial spirit that embraces challenges to push boundaries and seeks to welcome professionals to its team that share the desire to make meaningful and impactful contributions to its mission.

Always on the cutting edge of technology, AIQ provides its talent all the opportunities to thrive and excel. Working at AIQ includes dealing with massive data sets, an AI infrastructure that is powered by the latest NVIDIA GPU cloud computing platform and access to limitless computing, storage and network resources.

All in all, being a Data-Scientist at AIQ gives the unique opportunity to contribute to the development of complete AI solutions going through all the following key steps : technical formalization of the business needs, database creation (QC, selection and labeling), data preprocessing, algorithms development and implementation, model performance evaluation, deployment in production and even continual learning of AI machines.

Responsibilities

  • Develop next generation AI-enabled software products for oil & gas clients;
  • Translate business objectives into actionable analyses and insights;
  • Formalize Oil & Gas problems into AI problems;
  • Contribute to the solution design, in collaboration with other data scientists, engineers and SMEs;
  • Data preparation : Extract, clean, audit and preprocess data for analysis;
  • Data QC : Analyze quality of data produced and proactively develop solutions to data quality issues;
  • Contribute to the creation of large-scale labeled databases leveraging our annotation team;
  • Develop data-driven algorithms and prototypes for classification, regression, anomaly detection, failure prediction and optimization;
  • Evaluate proposed AI solutions with respect to the project objectives;
  • Keep up to date with the latest technology trends;
  • Apply state-of-the-art AI techniques to improve existing solutions;
  • Deploy and maintain AI models in production;
  • Help prepare and visualize interim and final results of analyses;
  • Communicate ideas, plans, and results, effectively via oral presentations and written reports.
  • Educational Requirements

  • Master’s degree or or Ph.D. in Computer Science, Applied Mathematics, Statistics, or any AI-related field;
  • Western education is mandatory.
  • Requirements

  • Very strong mathematical and analytical skills;
  • Results-driven and proactive personality;
  • Excellent communication skills;
  • Ability to build AI models and to find impactful and actionable recommendations based on the model;
  • Ability to manage ambiguity, take initiative, and hit the ground running.
  • Experience

  • 3+ years of experience demonstrating depth and breadth in state-of-the-art machine-learning, deep-learning, computer-vision, natural language processing, signal processing, or other AI technologies;
  • Relevant experience in industry or academia;
  • Demonstrated experience in developing core AI algorithms in industry or for real-world problems;
  • Demonstrated relevant experience in implementing robust and scalable industrial AI solutions;
  • Experience in the oil & gas exploration & production company or oil field services company is a plus.
  • Key Skills

  • Strong background in applied mathematics, algorithms and coding;
  • Proficient in statistics, machine-learning or deep-learning;
  • Strong background in AI application to computer-vision, NLP or signal-processing problems;
  • Proficient in at least one development language (e.g., Python), one data analysis library (e.g., Pandas) and either a deep-learning framework (e.
  • g., Pytorch, Tensorflow) or a machine-learning library (e.g., Scikit-learn);
  • Theoretical and at least practical knowledge of popular machine-learning algorithms (PCA, Support Vector Machines, RandomForest, XGBoost, etc.
  • or deep-learning networks (RNNs, LSTMs, CNNs, shallow networks, GANs, Transformers, etc.);
  • Hands-on with useful development tools (PyCharm, Jupyter, Docker, Git, etc);
  • Excellent communication, verbal and written skills.
  • Recommended Skills

    TensorflowScikit LearnPandasDeep LearningJupyterXgboost

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