Data Scientist Engineer, (Python, Data Bricks, SQL) - Complex Projects - Customer Behavior - Abu DhabiThis role requires a Data Scientist to be able to really \'Deep Dive\' into Data Science Theories.

• Understand and analyze \'Customer Life Time Value\'
• Applied Customer Behavior
• Applied Customer Pricing
• Marketing & Operations
• AB Testing & Statistical Testing
• How to interpret models and improve them
• Built Models and Used Cases
• MMM - Mix Media Modeling
• Customer Segmentation
• Use case roadmap
• Leading exploratory analyses to identify trends that will drive value for the business,
• Allocating critical resources, and monitoring the performance based on KPIs.
• Involves generating and supervising analytical approaches / models to optimize datadriven findings.

This role will be responsible to build, deploy and operationalize end-to-end Data science solutions in the Data Warehouse CloudResponsibilities

• Establish data science project plan to enable the strategy and roadmap, and supervise ongoing data science projects utilizing required methodologies such as time-series analyses, hypothesis testing, classification models and causal analyses.
• Lead and manage advanced and non-routine data science projects including origination, qualification, collection of data, data cleaning and results validation in close collaboration with the respective business owners
• Develop comprehensive understanding of data structures and metrics, advocating for changes where needed for both platform development and data analytics.
• Support the Decision Analytics Director to drive the fundamental shift in how the business decisions can be made based on data science findings to deliver increased value and business outcomes, driving overall enterprise revenue growth.
• Coaching the data scientist and create best data science practice user-guide.
• Work with counterparts in data technology and data intelligence department to drive business decisions by managing exploratory data analyses.

Qualifications

• 4+ Years Experience in Data Science
• Advanced knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks) and their real-world advantages/drawbacks
• Profound understanding of the current and future technology trends and their inter-operability, especially business analytics and decision-making methodologies
• Experience in manipulating, processing, and extracting value from large disconnected datasets
• Excellent in programming with Python, R, or similar programming language
• Proficiency in Microsoft Azure stack and the corresponding data analytic and data management tools such as DataBricks, AzureML, ADF, ADLS.
• Proficiency in distributed databases and query languages such as SQL or HQL (Snowflake)
• Profound knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage.) and experience with application
• Advanced knowledge in relevant data techniques, including statistical methods, distributed databases, query languages

Kingston Stanley