Assistant Vice President – Data Science

Role Overview

Assistant Vice President – Data Science at Aldar Properties. This role designs, builds, and productionizes advanced analytics and AI solutions that drive measurable business value through close collaboration with Data Product Owners, Data Analysts, BI Developers, and Data Quality Specialists.

Role Purpose

To translate complex business challenges into data-driven models and experiments using Python, SQL, and modern ML frameworks, developing scalable machine learning solutions that are explainable, governed, and aligned with organizational priorities.

Key Responsibilities

Strategy & Problem Definition

• Translate business problems into data science projects by defining clear hypotheses, success metrics, and validation methods.
• Work with Data Product Owners to define business outcomes, monitor model performance post-deployment, and ensure continued relevance.

Data Preparation & Exploration

• Explore, clean, and transform structured and unstructured data using Python and SQL to prepare high-quality datasets for modeling.
• Collaborate with Data Quality Specialists to ensure input data meets quality, lineage, and governance standards.

Model Development & Evaluation

• Design, train, and evaluate machine learning models using appropriate algorithms and statistical techniques including regression, classification, clustering, NLP, and forecasting.
• Conduct and analyze A/B tests or controlled experiments to assess model and feature performance.

Model Explainability & Governance

• Apply model explainability, fairness, and interpretability techniques such as SHAP, LIME, and feature importance to ensure transparency and accountability.
• Support AI governance activities, including Model Risk Management processes, ensuring compliance and responsible use of AI.

Productionization & Operations

• Collaborate with engineering and platform teams to productionize AI models through reproducible pipelines and CI/CD workflows.

Communication & Stakeholder Management

• Communicate results effectively through visualizations, storytelling, and presentations tailored to technical and non-technical audiences.

Qualifications & Experience

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Master's degree or higher in Data Science, Machine Learning, or Applied Statistics is an advantage.
• 6+ years of experience in data science, applied machine learning, or advanced analytics.
• Proven experience delivering models that have been deployed and integrated into business processes or digital products.
• Experience working in agile, cross-functional teams with Product Owners, Data Analysts, and Data Engineers.

Skills & Competencies

• Advanced proficiency in Python including pandas, NumPy, scikit-learn, XGBoost, and LightGBM.
• Strong command of SQL for data extraction, transformation, and validation.
• Experience working in Databricks or equivalent data and ML platforms.
• Familiarity with deep learning frameworks including PyTorch and TensorFlow.
• Familiarity with ML lifecycle tools including MLflow, Airflow, Docker, and Kubernetes.
• Understanding of model explainability, ethics, fairness, and governance.
• Knowledge of AI governance processes and documentation standards, including Model Risk Management.
• Exposure to cloud platforms including Azure and Snowflake.
• Experience with APIs for integrating AI models into applications.
• Familiarity with version control including Git/GitHub and collaborative coding practices.
• Excellent communication skills to explain technical findings in clear business terms.
• Collaborative and curious mindset with a passion for continuous learning and innovation.