Lead, Data Scientist- AIQ

About AIQ

AIQ is a new joint venture between ADNOC and Group 42, dedicated to developing artificial intelligence technologies in the United Arab Emirates. It focuses on creating and commercializing AI products and applications for the oil and gas industry, aiming to provide end-to-end solutions by leveraging its data, cloud, and talent to reduce costs and generate revenue for clients. AIQ fosters an innovative and entrepreneurial environment, providing access to massive datasets, an AI infrastructure powered by NVIDIA GPU cloud computing, and limitless computing, storage, and network resources. As a Data Scientist at AIQ, you will contribute to the development of complete AI solutions, from technical formalization of business needs, database creation, data preprocessing, algorithm development and implementation, to model performance evaluation, deployment, and continual learning.

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.
• Perform data preparation: Extract, clean, audit, and preprocess data for analysis.
• Conduct Data QC: Analyze data quality 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 project objectives.
• Stay updated with the latest technology trends and 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 Ph.D. in Computer Science, Applied Mathematics, Statistics, or any AI-related field.
• Western education is mandatory.

Requirements

• Willingness to be technically involved in algorithm development (design, coding, integration).
• Capability to manage teams of at least 2 Data Scientists.
• 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

• +4 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.
• Experience with management of teams of at least 2 Data Scientists.
• 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 (e.g., ExxonMobil, Chevron, Total, Shell, BP, Schlumberger, Halliburton, Baker Hughes) is a plus.

Key Skills

• Soft skills and teamwork: Excellent communication, verbal, and written 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 practical knowledge of popular machine-learning algorithms (e.g., PCA, Support Vector Machines, RandomForest, XGBoost) or deep-learning networks (e.g., RNNs, LSTMs, CNNs, shallow networks, GANs, Transformers).
• Hands-on experience with useful development tools (e.g., PyCharm, Jupyter, Docker, Git).