Senior Data Scientist | Corporate Services | Group Tech & Dig Platforms

Job Requisition ID: 180008 Established in the 1930s as a trading business, Al-Futtaim Group today is one of the most diversified and progressive, privately held regional businesses headquartered in Dubai, United A”rab Emirates. Structured into five operating divisions; automotive, financial services, real estate, retail and healthcare; employing more than 35,000 employees across more than 20 countries in the Middle East, Asia and Africa, Al-Futtaim Group partners with over 200 of the world's most admired and innovative brands. Al-Futtaim Group’s entrepreneurship and relentless customer focus enables the organisation to continue to grow and expand; responding to the changing needs of our customers within the societies in which we operate. By upholding our values of respect, excellence, collaboration and integrity; Al-Futtaim Group continues to enrich the lives and aspirations of our customers each and every day. Overview Of The Role: The Senior Data Scientist will lead the development of advanced machine learning models to optimize demand forecasting and pricing strategies, driving increased profitability and customer satisfaction. This role requires a high level of expertise in Generative AI, MLOps, and cross-functional collaboration to ensure the seamless integration of data-driven solutions within the business. Success in this role is measured by the accuracy and robustness of predictive models, the optimization of pricing strategies, and the overall enhancement of customer analytics. What You Will Do: - Design, develop, and deploy demand forecasting models using traditional and machine learning techniques like ARIMA, XGBoost, and LSTM. - Leverage Generative AI (GenAI) to enhance demand forecasting accuracy by simulating future demand scenarios and improving model predictions. - Implement dynamic pricing models using advanced algorithms to optimize pricing strategies based on demand predictions, inventory levels, and competitor data. - Analyze customer behavior by applying clustering algorithms and predicting customer lifetime value (CLV) with machine learning models to drive personalized pricing strategies. - Monitor, evaluate, and fine-tune model performance using metrics such as MAE, RMSE, and Precision/Recall, continuously improving model accuracy and robustness. - Apply MLOps practices, including version control, model deployment pipelines, and automated testing, to ensure seamless integration, monitoring, and scaling of models across environments. - Collaborate with cross-functional teams, including marketing, product, and sales, to ensure alignment between forecasting models and business objectives. - Deploy and scale forecasting and pricing models on Azure cloud platform, leveraging CI/CD pipelines and model monitoring tools for real-time performance tracking and proactive issue resolution. Required Skills To Be Successful: - Advanced machine learning model development and optimization skills. - Expertise in Generative AI techniques for demand forecasting. - Proficiency in MLOps practices and model deployment pipelines. - Strong communication and collaboration abilities with cross-functional teams. What Qualifies You For The Role: - Master's degree in Statistics, Data Science, Machine Learning, Mathematics, Computer Science, or a related quantitative field. - 10+ years of experience in data science, with at least 5+ years focused on demand forecasting, price optimization, and customer analytics. - Strong hands-on experience with ARIMA, XGBoost, LSTM, and OR models. - Proficiency in Python, SQL, Databricks, and cloud platforms (Azure, GCP). We’re here to provide excellent service but a little help from you can ensure a five-star candidate experience from start to finish. Before you click “apply”: Please read the job description carefully to ensure you can confidently demonstrate why this opportunity is right for you and take the time to put together a well-crafted and personalised CV to further boost your visibility. Our global Talent Acquisition team members are all assigned to specific businesses to ensure that we make the best matches between talent and opportunities. We not only consider the requisite compatibility of skills and behaviours, but also how candidates align with our Values of Respect, Integrity, Collaboration, and Excellence. As part of our candidate experience promise, we also want to make ourselves available to you throughout the application process. We make every effort to review and respond to every application.

Master's degree in Statistics, Data Science, Machine Learning, Mathematics, Computer Science, or a related quantitative field. 10+ years of experience in data science, with at least 5+ years focused on demand forecasting, price optimization, and customer analytics. Strong hands-on experience with ARIMA, XGBoost, LSTM, and OR models. Proficiency in Python, SQL, Databricks, and cloud platforms (Azure, GCP).

Design, develop, and deploy demand forecasting models using traditional and machine learning techniques like ARIMA, XGBoost, and LSTM. Leverage Generative AI (GenAI) to enhance demand forecasting accuracy by simulating future demand scenarios and improving model predictions. Implement dynamic pricing models using advanced algorithms to optimize pricing strategies based on demand predictions, inventory levels, and competitor data. Analyze customer behavior by applying clustering algorithms and predicting customer lifetime value (CLV) with machine learning models to drive personalized pricing strategies. Monitor, evaluate, and fine-tune model performance using metrics such as MAE, RMSE, and Precision/Recall, continuously improving model accuracy and robustness. Apply MLOps practices, including version control, model deployment pipelines, and automated testing, to ensure seamless integration, monitoring, and scaling of models across environments. Collaborate with cross-functional teams, including marketing, product, and sales, to ensure alignment between forecasting models and business objectives. Deploy and scale forecasting and pricing models on Azure cloud platform, leveraging CI/CD pipelines and model monitoring tools for real-time performance tracking and proactive issue resolution.