AldarAssistant Vice President – Data Science
AI in this role
JOB PURPOSE
As an AVP– Data Science, you will design, build, and productionize advanced analytics and AI solutions that drive measurable business value. You will work closely with Data Product Owners, Data Analysts, BI Developers, and Data Quality Specialists to translate complex business challenges into data-driven models and experiments. Leveraging Python, SQL, and modern ML frameworks, you will develop scalable machine learning solutions that are explainable, governed, and aligned with organizational priorities.
ROLES AND RESPONSIBILITIES
Translate business problems into data science projects by defining clear hypotheses, success metrics, and validation methods. Explore, clean, and transform structured and unstructured data using Python and SQL to prepare high-quality datasets for modeling. Design, train, and evaluate machine learning models using appropriate algorithms and statistical techniques (e.g., regression, classification, clustering, NLP, forecasting). Collaborate with engineering and platform teams to productionize AI models through reproducible pipelines and CI/CD workflows. Apply model explainability, fairness, and interpretability techniques (e.g., SHAP, LIME, feature importance) to ensure transparency and accountability. Support AI governance activities, including Model Risk Management (MRM) processes, ensuring compliance and responsible use of AI. Conduct and analyze A/B tests or controlled experiments to assess model and feature performance. Work with Data Product Owners to define business outcomes, monitor model performance post-deployment, and ensure continued relevance. Collaborate with Data Quality Specialists to ensure input data meets quality, lineage, and governance standards. Communicate results effectively through visualizations, storytelling, and presentations tailored to technical and non-technical audiences.RELATED YEARS OF EXPERIENCE
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.
FIELD OF EXPERIENCE
Analytics, Data Science, Artificial Intelligence
TECHNICAL AND INTERPERSONAL SKILLS
Advanced proficiency in Python (pandas, NumPy, scikit-learn, XGBoost, 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 (PyTorch, TensorFlow) and ML lifecycle tools (MLflow, Airflow, Docker, Kubernetes). Understanding of model explainability, ethics, fairness, and governance. Knowledge of AI governance processes and documentation standards, including Model Risk Management (MRM). Exposure to cloud platforms (Azure, Snowflake) and APIs for integrating AI models into applications. Familiarity with version control (Git/GitHub) and collaborative coding practices. Excellent communication skills to explain technical findings in clear business terms. Collaborative and curious, with a passion for continuous learning and innovationQUALIFICATION
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.How we rate this
Assistant Vice President – Data Science at Aldar rates 97 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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Skills and AI tools this role asks for
Questions you could be asked
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- Walk me through how you've used PyTorch in your day-to-day work.
- What are the limits of TensorFlow that you've run into, and how did you work around them?
- What's a project where you used scikit-learn hands-on?
- Walk me through how you've used XGBoost in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: NLP, PyTorch, TensorFlow, scikit-learn, and XGBoost. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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