Job Description

Job Requisition ID:

[[167300]]


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:

We are seeking a talented Senior Data Scientist to join our team and work on a project focused on demand forecasting, price optimization, and customer analytics. In this role, you will develop machine learning models to predict future demand patterns, optimize pricing strategies using dynamic pricing techniques, and analyze customer behavior to drive data-driven decisions. Your expertise will help enhance inventory management, pricing strategies, and overall customer satisfaction, contributing to the company's growth and profitability.

What you will do:

Strategic problem Solving Communication & Stake Holder management Model Development, Deployment & Maintenance AI Innovation: AI use cases identification and development Design, develop, and deploy demand forecasting models using traditional and machine learning techniques like ARIMA, XGBoost, and LSTM. Implement dynamic pricing models using advanced algorithms to optimize pricing strategies based on demand predictions, inventory levels, and competitor data. Leverage Generative AI (GenAI) to enhance demand forecasting accuracy by simulating future demand scenarios and improving model predictions. 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. Familiarity with tools and methods to explain machine learning models, such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations), to improve trust in model predictions. 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:

Experience as a Data Scientist Experience in Machine Learning 5+ years of experience in Cloud - Databricks Industry experience in Retail, FMCG, or Automotive

About the Team:

You will be reporting to Data Science Lead

What equips you for the role:

A 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 developing and deploying demand forecasting, price optimization, and customer analytics solutions. Strong hands-on experience with machine learning models such as ARIMA, XGBoost, LSTM, and price elasticity, and other classification model. 4+ years experience in Retail/Automotive/FMCG industry. Expertise in Generative AI (GenAI) techniques and applying them to demand forecasting and optimization scenarios. Proficiency in Python, SQL, Databricks and Cloud platforms (e.g., Azure, GCP), and familiarity with data processing frameworks like Spark. In-depth knowledge and experience with MLOps practices, including model versioning, deployment pipelines, automated testing, and monitoring in production environments. Experience with cloud-based model deployment and managing large-scale data pipelines, integrating real-time data for demand prediction and pricing optimization. Strong experience in feature engineering, model tuning, and performance monitoring using key metrics like MAE, RMSE, R and others. Excellent problem-solving skills and the ability to develop innovative solutions for complex forecasting and pricing challenges. Strong communication and collaboration skills to work effectively with cross-functional teams (marketing, product, sales) and stakeholders to drive impactful data-driven decisions. Familiarity with best practices in data security, scalability, and model interpretability for delivering high-performance, reliable solutions.
Hands-on experience with PyTorch and TensorFlow.

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why

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