Data Scientist - Onsite (Riyadh) - Octopus - Immediate hire
- robusta
- الرياض السعودية
- Full-time
وصف الوظيفة
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.
متطلبات الوظيفة
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.