Senior Data Scientist

مناطق رأس الخيمة الاقتصادية (راكز); Ras Al Khaimah; On-site; AED 200,000 - 300,000; Full time

Job summary

A leading economic zone in the UAE is seeking a Senior Data Scientist to lead advanced analytical model development and drive collaboration across various teams. The ideal candidate will have over 7 years of experience in data science, strong proficiency in Python and SQL, and a passion for AI/ML. This role offers significant influence on strategic decision-making processes and operational optimization.

Qualifications

• Deep understanding of machine learning and statistical modeling is essential.
• Proficiency in Python and SQL required.
• 7+ years of experience in applied data science expected.

Responsibilities

• Lead the development of advanced analytical models and AI/ML systems.
• Drive cross-functional collaboration with various stakeholders.
• Research and implement state-of-the-art techniques in AI/ML.

Skills

Machine learning algorithms

Statistical modeling

Experimentation design

Python

SQL

scikit-learn

XGBoost

TensorFlow

PyTorch

Communication skills

Education

Bachelor’s or Master’s degree in Data Science

PhD (plus)

Tools

SAP ERP

Salesforce CRM

Google Analytics

Job description

JOB PURPOSE

The Senior Data Scientist leads the development of advanced analytical models and AI/ML systems to power strategic decision-making and automation across RAKEZ. The role is central to building models that support lead scoring, customer retention, churn prediction, lifetime value estimation, and operational optimization.

This position will drive cross-functional collaboration with stakeholders in strategy, sales, finance, and operations while mentoring junior team members and advancing the maturity of RAKEZ’s data science capabilities.

CORE RESPONSIBILITIES

Data Integration & Exploratory Analysis

• Integrate, clean, and prepare data from multiple structured and unstructured sources including CRM, ERP, web analytics, and external datasets.
• Perform exploratory data analysis (EDA) to uncover trends, correlations, and anomalies.
• Generate insights and hypotheses to guide feature engineering and model selection.
• Work with data engineers to ensure data pipelines support model requirements and retraining strategies.

Feature Engineering & Data Preparation

• Engineer meaningful features from raw and semi-structured datasets to improve model accuracy and interpretability.
• Address data quality issues through imputation, outlier handling, normalization, and transformation techniques.
• Contribute to strategies for data augmentation and enrichment to enhance model input quality and generalizability.

Model Development & Experimentation

• Build, evaluate, and refine machine learning and statistical models for use cases such as lead conversion, churn risk, and customer segmentation.
• Design experiments (e.g., A/B testing) and perform hypothesis testing to validate model impact.
• Use appropriate modeling techniques (e.g., ensemble learning, regression, classification, clustering) based on business needs.

Explainability, Validation & Monitoring

• Generate and communicate explainable AI insights using tools like SHAP and LIME.
• Conduct rigorous model validation, cross-validation, and post-deployment monitoring.
• Partner with Data and ML Engineers to integrate monitoring and retraining pipelines.

Collaboration & Communication

• Translate business objectives into analytical workflows and deliver insights to non-technical stakeholders.
• Collaborate with data engineers, analysts, and product owners to align on delivery timelines and priorities.
• Present models and recommendations clearly to leadership and technical teams.

Research & Continuous Learning

• Research and implement state-of-the-art techniques and tools in machine learning, deep learning, and artificial intelligence to ensure that systems created are efficient and effective.
• Stay updated on industry trends, academic research, and emerging AI/ML methodologies.
• Recommend and implement novel approaches to improve model performance and business outcomes.

QUALIFICATIONS, EXPERIENCE, & SKILLS

Educational Qualifications

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Applied Mathematics, or related field.
• PhD is a plus and may substitute for years of work experience.

Professional Qualifications

• Deep understanding of machine learning algorithms, statistical modeling, and experimentation design.
• Proficiency in Python, SQL, and libraries like scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Familiarity with model interpretability techniques and tools (e.g., SHAP, LIME).
• Strong experience in source systems such as SAP ERP, Salesforce CRM, SaaS Applications, Google Analytics and structured/unstructured data sources.

Required Experience

• 7+ years of experience in applied data science, ideally within CRM, finance, or operational use cases.
• Experience in building production-grade models and partnering with MLOps for deployment.
• Proven track record of delivering business value from data science initiatives.

Other requirements

• Excellent communication skills with both technical and business stakeholders.
• Passion for solving real-world problems using AI/ML.
• Experience in agile delivery environments is preferred.