SP
Salla E-Commerce PlatformSenior Data Scientist – Generative AI
Senior/Staff Data Scientist developing AI for commerce in the Middle East. Architecting systems for merchant and customer AI assistants and content generation.
About the role
- Design and develop end-to-end ML solutions — from data exploration and feature engineering to model training, validation, and deployment.
- Collaborate cross-functionally with engineers, analysts, and product teams to integrate predictive and recommendation models into customer-facing and internal applications.
- Implement scalable ML pipelines using Databricks, PySpark, and Delta Lake, ensuring reproducibility, performance, and maintainability.
- Run controlled experiments (A/B tests, uplift modelling, causal inference) to measure model performance and quantify business impact.
- Operationalise models through CI/CD and MLOps best practices, including model versioning, monitoring, retraining strategies, and governance.
- Monitor production systems for drift, performance degradation, and anomalies, applying explainability and fairness techniques where needed.
- Contribute to the development of feature stores and reusable data assets to accelerate experimentation and deployment cycles.
- Stay current with emerging trends in ML, MLOps, and cloud data technologies to continuously improve model accuracy, scalability, and efficiency.
Requirements
- 5+ years of hands-on experience applying machine learning and statistical modelling in production or product-oriented environments.
- Proven understanding of the full spectrum of ML techniques — from traditional models (linear/logistic regression, tree-based methods, ensemble learning) to modern deep learning architectures (CNNs, RNNs, transformers, graph neural networks, diffusion and foundation models).
- Strong experience in Python, with proficiency in Scikit-learn, Autogluone, PyTorch or TensorFlow, and PySpark MLlib.
- Demonstrated ability to design scalable ML pipelines and automate workflows with MLOps tools (MLflow, Kubeflow, Databricks ML runtime, AWS Sagemaker, or AWS Bedrock).
- Familiarity with retrieval-augmented generation (RAG) and fine-tuning of large language models is a plus.
- Proficiency in SQL and distributed data frameworks, with experience in feature engineering at scale.
Benefits
- Mental health and well-being initiatives
- Generous parental (including secondary) leave policy
- Flexibility to work in a Hybrid model (2-3 days in-office)
- Paid birthday, study and volunteering leave every year
- Sponsored social clubs, team events, and celebrations
- Employee Resource Groups (ERG) to help you connect and get involved
- Investment in your personal growth offering training for your advancement
Job title
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Experience level
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Not specified
Degree requirement
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