Job Description
About Walmart:
Fifty years ago, Sam Walton started a single mom-and-pop shop and transformed it into the world's biggest retailer. Since those founding days, one thing has remained consistent: our commitment to helping our customers save money so they can live better. Today, we're reinventing the shopping experience and our associates are at the heart of it. You'll play a crucial role in shaping the future of retail, improving millions of lives around the world. This is that place where your passions meet purpose. Join our family and create a career you're proud of.
Job Description
What you'll do...
Looking for a new role that will be both challenging and rewarding, then continue reading to see if this position grabs your interest. The Manager of Seller Fees & Incentives Data and Analytics will drive the data and analytics function for the pricing lever for our business – seller fees and incentives.
You’ll sweep us off our feet if:
- You’re a strategic and structured problem-solver at heart.
- You’re organized, detail-oriented, disciplined, and can manage multiple projects simultaneously.
- You have strong communication skills and can translate complex data concepts and analyses into simple and easy to understand language.
- You have strong exploratory data analysis skills, ability to make analytical, data driven recommendations and a track record of taking ownership.
- You have proven experience in translating business requirements into innovative data solutions that drive growth and leveraging data to inform business strategy and process design.
- You are curious and seek to understand business problems beyond the task or data.
- You like building lasting partnerships, working cross-functionally, and tackling problems across a range of analytical topics.
- You accept and thrive in constantly evolving, fast-paced, multi-dimensional environments.
- You are curious, proactive, and comfortable working in an unstructured setup.
- You’re stimulated by challenges and are ready to engage at a Fortune 1 scale.
You’ll make an impact by:
- Building Solid Fees & Incentives Data Foundation for US Marketplace.
- Build capabilities to store historical WMT seller fees and develop tools to scrape and store competitive fee data.
- Build and pressure testing Incremental models to assess fee incentives ROI.
- Leverage incentives data to create a data lake of fee incentives rules spend data, ROI, and KPIs.
- Translating Data into Insights, Strategies, and Actions for X-functional Teams.
- Conduct exploratory data analyses to generate insights and identify patterns in fees and incentives data.
- Develop and implement causal and statistical analyses and models to determine drivers of incentives spend and ROI, predict outcomes, and enerate and communicate insights to inform/influence business decisions.
- Distill large, complex data sources into recommendations for x-functional teams and leadership to understand and navigate easily.
- Build dashboards to visualize incentives data and insights.
- Supporting Business Case Creation for Incentive Programs
- Engage with/ Business Leads, Seller Account Managers, and Program Managers to understand business needs and support the design of fee-driven incentive programs aimed at driving target seller behavior and delivering business results.
- Support x-functional partners to build business cases (including financial model), and define specific & trackable KPIs.
- Building Data Tools to Support Incentives Management Process
- Leverage world-class incentives data, models, and feedback tools to help build the WMT incentives experience for internal teams and sellers.
- Support business teams by building centralized methodologies and data tools to streamline data pulling required for business case submission and incentive approval process.
- Performing SWAT-team Analysis for Fees and Incentives Topics
- Support Seller Fees and Incentives team with ad hoc fees and incentives analyses to inform strategic decisions and optimize incentives spend.
- Partnering with the Data Science Team on Recommendation Engines and Predictive Models
- Work with the Data Science team to build advanced analytics recommendation engines and predictive models required to enable incentive spending scaling for high ROI programs.
- Pressure test logic behind models and validate model outputs to ensure recommendations are grounded in reality.
- Collaborating with Product, Engineering on Fees Engine
- Support business leads and product management teams by integrating data signals into the incentives engine to support new incentive launches and enhance the incentives management process.
- Liaise with Business/Product teams to align on tech roadmap to enable scale, flexibility, and effectiveness in the management of our fee incentives spend.
Qualifications
- Bachelor’s degree in Statistics, Computer Science, Business, Economics, Finance, or related quantitative field.
- 2 years of experience in data analytics or related field roles (e.g., advances analytics, data science, management consulting, investment banking, strategy and operations, or similar role).
- Advanced SQL, Python/R, Google Big Query, and Google Cloud Platform.
- Experience with statistical methods and advanced modeling techniques.
- Experience working with and manipulating large data sets and writing basic SQL scripts.
- Experience working cross-functionally.
Preferred Qualifications
- MBA or master’s degree in Statistics, Computer Science, Business, Economics, Finance, or a related quantitative field.
- 4 years of experience in data analytics or related field role (e.g., management consulting, investment banking, strategy and operations, advances analytics, data science, or similar role)
- Experience manipulating large data sets, writing custom SQL/Python/R scripts, and working with/ Google Big Query, and Google Cloud Platform.
- Experience working with modern data visualization tools such as Looker and Tableau and communicating findings to an executive audience.
- Experience in translating business requirements into innovative data solutions that drive growth and leveraging data to inform business strategy and process design.
- Experience building and executing strategic business processes within a large organization including working with technology partners to deliver automated information solutions.
- Strong exploratory data analysis skills and ability to make analytical, data-driven recommendations based on insights from data.
Additional Information
At Walmart , we offer competitive pay as well as performance-based bonus awards and other great benefits for a happier mind, body, and wallet. Health benefits include medical, vision, and dental coverage. Financial benefits include 401(k), stock purchase, and company-paid life insurance. Paid time off benefits include PTO (including sick leave), parental leave, family care leave, bereavement, jury duty, and voting. Other benefits include short-term and long-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement, and more.
You will also receive PTO and/or PPTO that can be used for vacation, sick leave, holidays, or other purposes. The amount you receive depends on your job classification and length of employment. It will meet or exceed the requirements of paid sick leave laws, where applicable.
For information about PTO, see .
Live Better U is a Walmart-paid education benefit program for full-time and part-time associates in Walmart and Sam's Club facilities. Programs range from high school completion to bachelor's degrees, including English Language Learning and short-form certificates. Tuition, books, and fees are completely paid for by Walmart.
Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to a specific plan or program terms.
For information about benefits and eligibility, see One.Walmart .
The annual salary range for this role is: $96,000.00-$186,000.00
Primary Location...
221 River St, Hoboken, NJ 07030, United States of America
Job Tags
Holiday work, Full time, Temporary work, Part time,