# Data Scientist at Qatar Airways

Qatar Airways is hiring a Data Scientist in Doha, Qatar. Level rates it Builds AI ●●●●; you can [apply on Qatar Airways](https://jobsbylevel.com/go/85c0c174-8c91-41be-aaf0-99aa5fdab526?utm_medium=ai-agent).

AI Level 4, AI centrality 94 out of 100. Qatar-Doha.

## Details

- Company: [Qatar Airways](https://jobsbylevel.com/companies/qatar-airways)
- AI level: AI Level 4 (score 94 out of 100)
- Location: Qatar-Doha
- Posted: November 5, 2026
- Apply: https://jobsbylevel.com/go/85c0c174-8c91-41be-aaf0-99aa5fdab526?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=data-scientist-at-qatar-airways-97aea4

## Prepare for this job

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## Description

Description About the Role This role will play a critical role in helping to extract valuable insights from vast data sets and use those insights to make informed business decisions. Responsible for designing and implementing data mining algorithms, developing predictive models and machine-learning algorithms, and communicating findings to both technical and non-technical audiences. This role is also responsible for developing and deploying cutting-edge natural language processing and generative models to drive innovation and solve complex business challenges. Your duties would include: Analyze large volumes of structured/unstructured data to uncover trends, patterns, and hidden insights to identify opportunities for business problem statements and use cases. Utilize advanced statistical and machine learning techniques to extract meaningful insights from complex data sets and help business leaders make informed decisions. Develop and apply algorithms and models to derive insights from large-scale datasets, perform data and error analysis to improve model performance, ensure data quality and model accuracy through cleansing and validation, and implement model explainability to support transparent and interpretable outcomes . Demonstrate a strong understanding of AI/ML governance frameworks, responsible AI principles, and MLOps pipelines across public cloud platforms such as Google Cloud Platform (GCP) and Microsoft Azure. Design and implement data mining, machine learning, and time-series forecasting models on cloud platforms, ensuring alignment with enterprise AI security, AI governance, and Data compliance frameworks. Evaluate model performance through appropriate metrics, error analysis, hyperparameter tuning, validation, and iterative improvements, incorporating business and user feedback to enhance model accuracy, robustness, and effectiveness. Apply ensemble learning techniques and advanced modelling approaches to improve predictive performance, generalization, and model reliability. Leverage Generative AI and Large Language Models (LLMs) to develop innovative business solutions, including the responsible generation and use of synthetic data where appropriate. Design, develop, and deploy advanced Natural Language Processing (NLP) and LLM-based solutions for large-scale unstructured data, supporting use cases such as text generation, summarization, translation, information extraction, and content generation through effective prompt engineering techniques. Collaborate closely with business stakeholders to understand business challenges, translate requirements into analytical problems, and identify high-value Machine Learning, Generative AI, and Artificial Intelligence use cases. Partner with Data Engineering teams to acquire, integrate, cleanse, validate, and prepare high-quality datasets required for advanced analytics and AI/ML solutions. Operationalize analytical and AI/ML models by collaborating with Software Engineers, Data Engineers, Machine Learning Engineers, and MLOps Engineers, ensuring scalable, secure, monitored, and reliable production deployment. Communicate complex technical concepts, analytical findings, model outcomes, limitations, and recommendations effectively to both technical and non-technical stakeholders. Collaborate with cross-functional teams to integrate Data Science, Machine Learning, and Generative AI solutions into enterprise applications and business processes. Maintain awareness of emerging advancements in AI, Generative AI, LLMs, Machine Learning, and MLOps, and assess their applicability in solving real-world business problems. Evaluate Data Science, AI/ML, and Generative AI solutions proposed by vendors, assessing solution architecture, modelling methodology, technical feasibility, scalability, security, governance, explainability, MLOps readiness, cloud compatibility, performance metrics, and alignment with business requirements. Provide technical recommendations during vendor

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