Data Scientist - Onsite (Riyadh) - Octopus - Immediate hire

About Octopus by RTG

Octopus by RTG is the tech hiring and outsourcing arm of Robusta Technology Group, dedicated to connecting exceptional tech talent with top-tier organizations across the MENA, GCC, Europe, the US, and Canada. We specialize in building strong, long-term partnerships between skilled professionals and innovative companies. Our mission is to empower growth, innovation, and excellence by matching the right talent with the right opportunities. Currently, we are hiring a Data Scientist for one of our partner organizations in KSA on a 1-year contract, offering the opportunity to contribute to exciting projects within a dynamic and forward-thinking environment. Main Responsibilities

Identify and prioritize AI / ML use cases that deliver measurable business value and ROI. Develop, validate, and deploy machine learning models for fraud detection, claim risk prediction, customer segmentation, and pricing optimization. Build and maintain end-to-end data pipelines including data preparation, feature engineering, and model deployment. Implement advanced analytics techniques, including deep learning, NLP, and computer vision where applicable. Collaborate with ML Engineers to productionize models using MLOps best practices and CI / CD pipelines. Ensure compliance with Saudi regulations (PDPL, NDMO) for data usage, model development, and AI governance. Implement model monitoring, drift detection, and automated retraining pipelines to maintain model performance. Partner with business stakeholders to translate complex business problems into actionable data-driven solutions. Conduct A / B testing and experimentation to measure model effectiveness and optimize outcomes. Develop and deploy explainable AI (XAI) models ensuring transparency and regulatory compliance. Prepare technical documentation, analytical reports, and executive dashboards on model outcomes and insights. Lead knowledge transfer sessions and mentor junior data scientists and analysts to build internal capabilities. Stay current with latest AI / ML research and evaluate new technologies and techniques for business applications. Collaborate with the Data Engineering team to ensure high-quality, reliable data for ML models. Support the development of enterprise ML platforms and reusable AI / ML components. Requirements

Main Requirements

Education

Bachelor’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field (required). Master’s or PhD in Machine Learning, Data Science, Statistics, or Artificial Intelligence (preferred). Relevant certifications such as Google Cloud ML Engineer, AWS Certified Machine Learning, TensorFlow Developer, or Azure AI Engineer are a plus. Experience & Skills

5+ years of proven experience in Machine Learning, Data Science, and Applied Statistics. Strong expertise in Python and ML frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost). Proficiency in SQL and experience working with big data platforms (e.g., BigQuery, Spark, Databricks). Hands‑on experience with MLOps tools such as Kubeflow, MLflow, Vertex AI, or SageMaker. Demonstrated ability to work with structured and unstructured data (JSON, text, images). Deep understanding of statistical methods, experimental design, and hypothesis testing. Proven track record in model deployment, monitoring, and lifecycle management. Familiarity with cloud‑native ML platforms (Vertex AI, Azure ML, SageMaker). Knowledge of AI governance frameworks, explainable AI (XAI), and model interpretability. Experience with deep learning, computer vision, and natural language processing (NLP). Knowledge of generative AI, LLMs, and real‑time or edge ML deployment. Strong understanding of Saudi data regulations (PDPL, NDMO, SAMA). Experience in the insurance or financial services domain (fraud detection, pricing models, claims prediction). Excellent communication skills to explain complex analytical findings to non‑technical audiences. Strong leadership and mentoring abilities, with experience guiding data science teams.

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