Senior Data Scientist (Machine Learning)

Overview
About AIQ
AIQ is a 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.
About The Role
We are seeking an experienced Data Scientist to join our team operating in the Oil and Gas sector, specifically in upstream optimization. As a Data Scientist, you will play a crucial role in extracting insights from complex datasets, developing data-driven algorithms, and implementing AI solutions to optimize oil production operations. Your expertise in machine learning, deep learning, and optimization techniques will be vital in solving industry-specific challenges and driving efficiency gains in oil production processes. You will work closely with cross-functional teams to analyze data, implement, deploy and maintain solution, and deliver actionable recommendations to improve production management and maximize operational performance.
Responsibilities
Responsibilities

• Understanding business objectives and developing AI solutions that help to achieve them, along with metrics to track their progress.
• Prepare, clean, and preprocess data for analysis.
• Analyze data quality and proactively address issues.
• Develop data-driven algorithms for clustering, classification, regression, and optimization.
• Evaluate AI solutions aligned with business objectives.
• Deploy and manage AI models in production.
• 
  Identify differences in data distribution that could potentially affect model performance in real-world applications.
• Analyzing the errors of AI models and designing strategies to overcome them.
• Maintain and enhance existing solutions to meet evolving business needs.
• Visualize and communicate results analysis effectively.
• Present ideas, plans, and findings orally and in written reports.
• Collaborate with data scientists, data engineers, and software engineers on production applications.
  

Qualifications
Experience

• 5+ years of experience demonstrating depth and breadth in state-of-the-art machine-learning, deep-learning and optimization.
• Demonstrated experience in developing core AI algorithms in industry or for real-world problems.
• Proven track record of implementing robust and scalable industrial AI solutions.
• Strong understanding of the unique challenges and complexities involved in optimization.
• Experience in implementation of MLOps pipelines is a plus.
• Experience in the Oil & Gas industry is a plus.
  

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Key Skills

• Strong background in applied mathematics, algorithms, and coding.
• Proficiency in statistics, machine learning, and deep learning.
• Proficiency in Python programming and data analysis libraries (e.g., Pandas, NumPy).
• Proficiency in data manipulation, cleaning, preprocessing and feature engineering …
• Proficiency in deep learning frameworks (e.g. Keras, PyTorch).
• Theoretical and practical knowledge of popular machine learning algorithms (e.g., PCA, Support Vector Machines, RandomForest, XGBoost, skforecast).
• Theoretical and practical knowledge of popular optimization methodologies (ex. PSO, GA, SGD…).
• Experience with common development tools (e.g., PyCharm, Jupyter, Docker, Git).
• Excellent communication skills, both verbal and written.
  

Educational Requirements

• BSc or MSc degree in a relevant field (e.g., Computer Science, Statistics). PhD degree is a plus.