Overview
Digital Transformation (DT) is responsible for providing AI-powered solutions to various business functions within Aramco. The solutions leverage the latest technological advancements of Artificial Intelligence (AI) and analytics to create business values across the value chain. The DT AI&A organization will be a center of excellence in Saudi Aramco for Artificial Intelligence technologies and data science.
The data scientist primary role is to work closely with business functions, stakeholders, functional teams to give consultations on AI use case, identify potential value from data, formulate AI and data science ideas and conceptualize them. In addition, to building, evaluating and productionalizing models as appropriate.
Duties & Responsibilities
- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
- Mine and analyze data from company data sources to drive optimization and improvement of product development, and business strategies.
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
- Develop custom data models and algorithms to apply to data sets.
- Use predictive modeling to increase and optimize production facilities, revenue generation, and other targeted outcomes.
- Develop A/B testing mechanisms and test model quality and validate hypothesis accordingly.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Develop necessary documentation as per established standards.
Minimum Requirements
- You will hold a Master’s degree in data science, computer science, computer vision, applied mathematics or a related field from a recognized and approved program. A PhD degree is preferred.
- You must have at least 5 years of experience with hands-on data science, NLP, computer vision, and/or machine learning projects/products in industry.
- You must also be able to bring ideas from conceptualization to productionalization (putting models in production) using the right tools (e.g. mlflow, kubeflow, tensorlight, etc).
- Having very strong expertise in data collection, cleaning, preprocessing, and wrangling is a requirement.
- You must be fluent in either R or Python, preferably both, and familiarity with golang is a plus.
- Proficiency in visualization tools and packages; and in communicating data science topics to non-technical audience is a requirement.
- You must be experienced in Information Retrieval (Content Recommendation, Search Metrics, Search Query, document classification, entity recognition, topic modelling, etc).
- Proficiency in transfer learning and in fine tuning open-sourced models like BERT is necessary.