Function as the bridge between business operations and data. Operations Data Analytics uses methods and tools to drive value from the data and deliver insights & visualizations to lead the growth across the enterprise. This occurs by analyzing patterns and trends through the use of data analysis and a full understanding of the business requirements. Their work involves an in-depth understanding of each operational product. Leveraging big data for business to develop data-driven solutions, explicitly tailored toward the needs of the enterprise operations.

Data Analytics responsibilities include deep analysis of data and determining the best way to represent it visually to stakeholders. They also ensure quality measures, process documentation, and create Key Performance Indicators (KPIs) to monitor the enterprise's operating performance. Collaborate with data science and AI/GENAI projects for global operations.

Desing and implement soultions to eliminate repitative manual processes. Work on projects to expand automation in operations.

They are expected to work on small to large-scale projects and collaborate with technical, business units, and top management, and knowledge in data science to deliver insights, reports, and any requested information with a high accuracy level promptly.

Accountabilities and Key Roles:

• Use the right tools and methods to extract, mine data from its sources, deal with large datasets, preprocess it, apply explanatory analysis, and derive insights out of it
• Ensure to maintain the reliability and trustworthiness of data throughout its lifecycle
• Data mining or extracting usable data from valuable data sources, taking a lead in exploring and discovering data-driven opportunities.
• Maintain and publish standards service reports for all PLC.
• Present results using data visualization and storytelling techniques.
• Experience with SSAS (SQL Server Analysis Services) to build data models required to support the business with robust solutions that provide data, analytics, and reports, which support numerous functions including metrics, dashboards, and data discovery.
• Propose solutions and strategies to solve business challenges and recommendations to improve performance.
• Partner with other multi-functional teams, such as Operations Management, and IT to collaborate on projects.
• Partner with the Data Science team to design and implement machine learning predictive models, and process automation to provide enhancements and feedback on the operational processes.
• Accountable for data visualization and reporting access, updates, correction, and general administration used by the operations team.
• Locate and define new process improvement opportunities, recommend systems modifications, and develop policies for data governance

Job Requirements:

Education:

• Bachelor’s degree in Data Science or any related field from a recognized university.

Experience:

• 2+ years of experience in data analytics and building data models, Banking experience is a plus.

Competencies:

• Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of data with attention to detail and accuracy.
• Data storytelling: Communicate actionable insights using data, often for a non-technical audience and experience with visualization reporting tools (PowerBi, Tableau, etc).
• Strong skills in data modeling (SQL Server Analysis Services (SSAS Modeling)).
• Programming: Write computer programs and analyze large datasets to uncover answers to complex problems with a variety of languages such as Java, R, Python, and SQL.
• Knowledge of statistics and use of statistical concepts and packages for analyzing datasets (Excel, DAX, etc).
• Computer science: Apply the principles of artificial intelligence, database systems, human/computer interaction, numerical analysis, and software engineering.
• Machine learning: Implement algorithms and statistical models to enable a computer to automatically learn from data.
• Problem-solving Skills.
• Good communication skills with the ability to present technical details to a non-technical audience.
• Business intuition: Connect with stakeholders to gain a full understanding of the problems they’re looking to solve.
• Critical thinking: Apply objective analysis of facts before coming to a conclusion.
• Interpersonal skills: Communicate with a diverse audience across all levels of an organization.