The Data Scientist is responsible for developing and deploying advanced analytics and machine learning solutions to support data-driven business decisions. The role involves managing the full model lifecycle, from data analysis and feature engineering to model deployment and performance monitoring, while transforming complex data into actionable business insights.
Ready to apply for roles like this?
Unlock the company name and direct application link. Subscribers get instant access to fresh jobs across Dubai, Abu Dhabi and Riyadh, many with visa support.
Unlock employer & apply directly
Responsibilities
- Design, build, train, and validate predictive and prescriptive models using statistical, machine learning, and optimization techniques.
- Perform variance attribution and root-cause analysis to explain deviations and inform performance improvement actions.
- Deploy AI-enabled solutions to streamline analytical workflows and accelerate decision-making processes.
- Work with data engineers to ensure data pipelines, feature stores, and ETL processes support modeling needs.
- Collaborate with platform and ML engineering teams to operate models in production environments through automated pipelines and APIs.
- Implement MLOps frameworks for version control, documentation, reproducibility, performance monitoring, and retraining.
- Champion data-driven decision-making and foster analytical literacy across the organization.
- Contribute to internal knowledge repositories, development of reusable assets, accelerators, and internal best-practice frameworks to improve time-to-value.
Requirements
- Bachelor’s or Master’s degree in data science, statistics, applied mathematics, computer science, or related fields.
- 5 - 8 years of experience in data science, machine learning, or applied analytics, within a complex or large-scale organization.
- PhD preferred or equivalent experience demonstrating advanced research capability.
- Strong proficiency in Python or R for modeling and analysis, along with fluency in SQL or PySpark for data extraction and manipulation.
- A deep understanding of statistical methods, time-series forecasting, regression modeling, and supervised and unsupervised learning techniques is essential.
- Experience with generative AI or NLP frameworks (Claude Sonnet, OpenAI APIs, Hugging Face, LangChain) is considered an advantage.
