Senior, Data Analyst

Position Summary:Data Source Identification: Requires knowledge of Functional business domain and scenarios; Categories of data and where it is held; Business data requirements; Database technologies and distributed datastores (e.g. SQL, NoSQL); Data Quality; Existing business systems and processes, including the key drivers and measures of success. To support the understanding of the priority order of requirements and service level agreements. Help identify the most suitable source for data that is fit for purpose. Perform initial data quality checks on extracted data.Data Strategy: Requires knowledge of understanding of business value and relevance of data and data enabled insights / decisions; Appropriate application and understanding of data ecosystem including Data Management, Data Quality Standards and Data Governance, Accessibility, Storage and Scalability, etc.; Understanding of the methods and applications that unlock the monetary value of data assets. To understand, articulate, and apply principles of the defined strategy to routine business problems that involve a single function.Understanding Business Context: Requires knowledge of Industry and environmental factors; Common business vernacular; Business practices across two or more domains such as product, finance, marketing, sales, technology, business systems, and human resources and in-depth knowledge of related practices; Directly relevant business metrics and business areas. To Provide recommendations to business stakeholders to solve complex business issues. Develop business cases for projects with a projected return on investment or cost savings. Translate business requirements into projects, activities, and tasks and aligns to overall business strategy and develops domain specific artifact. Serve as an interpreter and conduit to connect business needs with tangible solutions and results. Identify and recommend relevant business insights pertaining to their area of work.Tech. Problem Formulation: Requires knowledge of Analytics/big data analytics / automation techniques and methods; Business understanding; Precedence and use cases; Business requirements and insights. To translate/ co-own business problems within one's discipline to data related or mathematical solutions. Identify appropriate methods/tools to be leveraged to provide a solution for the problem. Share use cases and gives examples to demonstrate how the method would solve the business problem.Data Visualization: Requires knowledge of Visualization guidelines and best practices for complex data types; Multiple data visualization tools (for example, Python, R libraries, GGplot, Matplotlib, Ploty, Tableau, PowerBI etc.); Advanced visualization techniques/ tools; Multiple story plots and structures (OABCDE); Communication & influencing technique; Emotional intelligence. To generate appropriate graphical representations of data and model outcomes. Understand customer requirements to design appropriate data representation for multiple data sets. Work with User Experience designers and User Interface engineers as required to build front end applications. Present to and influences the team and business audience using the appropriate data visualization frameworks and conveys clear messages through business and stakeholder understanding. Customize communication style based on stakeholder under guidance, and leverages rational arguments. Guide and mentor junior associates on story types, structures, and techniques based on context.Data Quality Management: Requires knowledge of Data quality management techniques and standards; Business metadata definitions and content data definitions; Data profiling tools, data cleansing tools, data integration tools, and issues and event management tools; Understanding of user's data consumption, data needs, and business implications; Data modeling, storage, integration, and warehousing; Data quality framework and metrics; User access best practices; Enterprise data architecture, modeling and design, storage, integration, and warehousing; Enterprise data quality framework and metrics; Enterprise data strategy; Enterprise data quality strategy; Enterprise strategy to address regulatory and ethical requirements and policies around data privacy, security, storage, retention, and documentation. To promote and educate others on data quality awareness. Profile, analyze, and assess data quality. Test and validate data quality requirements. Continuously measure and monitor data quality. Deliver against data quality service level agreements. Manage operational Data Quality Management procedures. Manage data quality issues and leads data cleansing activities to remove data quality defects, improve data quality, and eliminate unused data. Determine user accessibility and removes or restricts user access as needed. Interpret company and regulatory policies on data. Educate others on data governance processes, practices, policies, and guidelines.Exploratory Data Analysis: Requires knowledge of relevant Knowledge Discovery in Data (KDD) tools, applications, or scripting languages such as SQL, Oracle, Apache Mahout, MS Excel, Python; Statistical techniques (for example, mean, mode, median, variance, standard deviation, correlation, and sorting and grouping); Research analysis standards and activities; Documentation procedures such as drafting, editing, Bibliography format; Relevant Knowledge Discovery in Data (KDD) tools, applications, or scripting languages such as SQL, DB, SAS, Oracle, Apache Mahout, MS Excel, Python; KDD industry best practices and emerging trends. To collect and tabulate data and evaluate results to determine accuracy, validity, and applicability. Support the identification and application of statistical techniques based on requirements. Apply suitable technique under direction from leadership. Assist in the planning, design and implementation of an exploratory data analysis research projects. Understand existing statistical models and identify and recommend statistical models based on hypothesis. Use advanced Knowledge in Data Discovery tools to write queries and analyze data to identify patterns, trends, outliers, and correlations. Conduct statistical analysis (for example hypothesis tests, confidence intervals) and build basic statistical models using relevant packages/software suites.Minimum Qualifications:Option 1: Bachelor's degree in Business, Engineering, Statistics, Economics, Analytics, Mathematics, Arts, Finance or related field and 2 years' experience in data analysis, data science, statistics, or related field. Option 2: Master's degree in Business, Engineering, Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field. Option 3: 4 years' experience in data analysis, data science, statistics, or related field.Preferred Qualifications:Data science, data analysis, statistics, or related field, Master’s degree in Business, Computer Science, Engineering, Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field, Related industry experience (for example, retail, merchandising, healthcare, eCommerce), Successful completion of assessments in data analysis and Business Intelligence tools and scripting languages (for example, SQL, Python, Spark, Scala, R, Power BI, or Tableau).Primary Location:311 North Walton Boulevard, Bentonville, AR 72716, United States of America

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