Senior, Data Analyst

Walmart•Bentonville, AR

9h•$80,000 - $155,000•Onsite

About The Position

What you'll do... Position: Senior, Data Analyst Job Location: 1 Customer Drive, Bentonville, AR 72716 Duties: Tech. Problem Formulation: 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. Understanding Business Context: 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. Data Source Identification: 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 Visualization: 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: 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: 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. Data Strategy: understand, articulate, and apply principles of the defined strategy to routine business problems that involve a single function.

Requirements

Master’s degree or the equivalent in Engineering, Analytics, Computer Science or a related field plus one (1) year of experience in data analysis, data science, statisics, or related experience OR Bachelor’s degree or the equivalent in Engineering, Analytics, Computer Science or a related field plus two (2) years of experience in data analysis, data science, statisics, or related experience.
Must have experience with: designing and building analytical reports and dashboards using different BI tools such as PowerBI or Tableau; performing data analysis from multiple data sources using SQL and Python; defining and monitoring the KPI’s of the applications using data visualization techniques; identifying the patterns and trends in the data using SQL and Python and performing root cause analysis; building and interpreting the predictive models using Python and KNIME; manipulating data and providing recommendations to the stakeholders from large data sets using Alteryx, SQL, and SAS; collaborating with different stakeholders and ensuring the prioritization of the product backlog and the implementation; building data pipelines from multiple data sources to migrate data to a common data platform using Python and SQL;.
maintaining the data integrity and accuracy across the product dashboards in PowerBI; and performing A/B testing to provide recommendations to improve the overall product performance.
Employer will accept any amount of experience with the required skills.

Responsibilities

Tech. Problem Formulation: 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.
Understanding Business Context: 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.
Data Source Identification: 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 Visualization: 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: 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: 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.
Data Strategy: understand, articulate, and apply principles of the defined strategy to routine business problems that involve a single function.