What you'll do...
Code Development and Testing: Requires knowledge of coding languages like SQL, Java, C++, Python and others; Testing methods such as static, dynamic, software composition analysis, manual penetration testing and others; Business, domain understanding. To write code to develop the required solution and application features by determining the appropriate programming language and leveraging business, technical, and data requirements. Create test cases to review and validate the proposed solution design. Create proofs of concept. Test the code using the appropriate testing approach. Deploy software to production servers. Contribute code documentation, maintain playbooks, and provide timely progress updates.
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 evaluate proposed business cases for projects and initiatives. Translate business requirements into strategies, initiatives, and projects and aligns them to business strategy and objectives, and drives the execution of deliverables. Build and articulate the business case and return on investment and delivers work that has demonstrable value. Challenge business assumptions on topics related to one's domain expertise. Mentor the team members on new business insights and allied developments. Proactively engage in the external community to build Walmart's brand and learn more about industry practices.
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 identify and recommend the most suitable visualization tools based on context. Generate appropriate graphical representations of data and model outcomes. Understand customer requirements to design appropriate data representation for complex data sets and drive User Experience designers and User Interface engineers to build front end applications. Define application design based on customer requirements. Build compelling stories based on context to integrate multiple pieces of information into cohesive insights. Present to and influence diverse audiences using the appropriate data visualization frameworks and conveys clear messages through deep business and stakeholder understanding. Customize communication style based on stakeholders and leverages relationships to drive behavioral change. Guide and mentor junior associates on story types, structures, and techniques based on context.
Model Assessment and Validation: Requires knowledge of model fit testing, tuning, and validation techniques (e.g., Chi square, ROC curve, root mean square error etc.); Impact of variables and features on model performance To identify and review model evaluation metrics based on analytical requirements. Apply suitable techniques for model testing and tuning, to assess accuracy, fit, validity, and robustness. Ensure testing information is documented and maintained by the team.
Model Deployment and Scaling: Requires knowledge of impact of variables and features on model performance; understanding of servers, model formats to store models. To deploy models or model ensemble and ensure sustainability and maintenance overtime. Implement model monitoring and model life-cycle management practices. Assist in creation of innovative user interfaces and support the use of models through collaboration with key stakeholders.
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, interpret, and apply principles of the defined strategy to complex business problems. Leverage experiences and learnings to inform and influence data strategy that typically spans one or more functions or domains.
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 collaborate with multiple business stakeholders to gain an understanding of complex organizational problems from varied perspectives to create effective technology focused solutions. Influence the business to redefine the problem statement. Design multi-stage, data-driven solutions to real-world problems for issues that are amenable to a data-driven solution. Redefine data analytics, big data analytics, automation goals, and deliverables by leveraging experience with the business problem. Identify return on investment.
Analytical Modeling: Requires knowledge of feature relevance and selection; Exploratory data analysis methods and techniques; Advanced statistical methods and best-practice advanced modelling techniques (e.g., graphical models, Bayesian inference, basic level of NLP, Vision, neural networks, SVM, Random Forest etc.); Multivariate calculus; Statistical models behind standard ML models; Advanced excel techniques and Programming languages like R/Python; Basic classical optimization techniques (e.g., Newton-Rapson methods, Gradient descent); Numerical methods of optimization (e.g. Linear Programming, Integer Programming, Quadratic Programming, etc.) To explore and create automated feature generation framework. Develop standard EDA process. Develop best practices on experimentation. Drive exploratory work in newer areas of Math, Statistics, Machine Learning, and Optimization Techniques. Continuously improve the business's data analysis models. Create industry-leading performance by leveraging new and creative data-sources. Employ the latest in machine learning in the department, Scope, design, and implement new machine learning models to support the business's initiatives and programs with a view of achieving overall objectives and targets. Guide teams in the development and delivery of big data predictive technologies models, and fully working prototypes of complex algorithms using readily available libraries.
Provides overall direction by analyzing business objectives and customer needs; developing, communicating, building support for, and implementing business strategies, plans, and practices; analyzing costs and forecasts and incorporating them into business plans; determining and supporting resource requirements; evaluating operational processes; measuring outcomes to ensure desired results; identifying and capitalizing on improvement opportunities; promoting a customer environment; and demonstrating adaptability and sponsoring continuous learning.
Develops and implements strategies to attract and maintain a highly skilled and engaged workforce by diagnosing capability gaps; recruiting, selecting, and developing talent; supporting mentorship, workforce development, and succession planning; and leveraging the capabilities of new and existing talent.
Cultivates an environment where associates respect and adhere to company standards of integrity and ethics by integrating these values into all programs and practices; developing consequences for violations or non-compliance; and supporting the Open Door Policy.
Develops and leverages internal and external partnerships and networks to maximize the achievement of business goals by sponsoring and leading key community outreach and involvement initiatives; engaging key stakeholders in the development, execution, and evaluation of appropriate business plans and initiatives; and supporting associate efforts in these areas.
Live our Values
• Models the Walmart values to foster our culture; holds oneself and others accountable; and supports Walmart's commitment to communities, social justice, corporate social responsibility, and sustainability; maintains and promotes the highest standards of integrity, ethics and compliance.
• Acts as an altruistic servant leader and is consistently humble, self-aware, honest, and transparent
Curiosity & Courage
• Demonstrates curiosity and a growth mindset; fosters an environment that supports learning, innovation, and intelligent risk-taking; and exhibits resilience in the face of setbacks.
Digital Transformation & Change
• Drives continuous improvements, supervises the adoption of new technology, and supports digital disruption in line with Walmart's business model.
Deliver for the Customer
• Delivers expected business results while putting the customer first and consistently applying an omni-merchant mindset and the EDLP and EDLC business models to all plans and initiatives.
• Adopts a holistic perspective that considers data, analytics, customer insights, and different parts of the business when making plans and implementing strategies.
Focus on our Associates
Diversity, Equity & Inclusion
• Supports strategies and drives initiatives that attract and retain diverse and inclusive talent; builds high-performing teams; embraces diversity in all its forms; and actively supports diversity goal programs.
Collaboration & Influence
• Builds strong and trusting relationships with team members and business partners; works collaboratively and cross-functionally to achieve objectives; and communicates with energy and positivity to motivate, influence, and inspire commitment and action.
• Creates a discipline and focus around developing talent, builds the talent pipeline, fosters an environment allowing everyone to bring their best selves to work, empowers associates and partners to act in the best interest of the customer and company, and regularly recognizes others' contributions and accomplishments.
Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.
Option 1: Bachelor's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 7
years' experience in an analytics related field. Option 2: Master's degree in Statistics, Economics, Analytics, Mathematics, Computer Science,
Information Technology or related field and 5 years' experience in an analytics related field. Option 3: 9 years' experience in an analytics or related
3 years' supervisory experience.
Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.
Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Supervisory experience, Using open source frameworks (for example, scikit learn, tensorflow, torch)
600 WEST CALIFORNIA AVENUE, SUNNYVALE, CA 94086-2486, United States of America