Manager, Data Science - AI Agent Development

What you’ll do…

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 the model evaluation metrics. Apply best practice techniques for model testing and tuning to assess accuracy, fit, validity, and robustness for multistage models and model ensembles.

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, Plotly, 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 influence the team and business audience using the appropriate data visualization frameworks and convey clear messages through business and stakeholder understanding. Customize communication style based on stakeholder (under guidance) and leverage rational arguments. Guide and mentor junior associates on story types, structures, and techniques based on context., 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 align to overall business strategy and develop domain-specific artifacts. 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.

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-Raphson methods, gradient descent), numerical methods of optimization (e.g., linear programming, integer programming, quadratic programming, etc.). To select appropriate modeling techniques for complex problems with large-scale, multiple structured and unstructured data sets, select and develop variables and features iteratively based on model responses in collaboration with the business. Conduct exploratory data analysis activities (for example, basic statistical analysis, hypothesis testing, statistical inferences) on available data. Identify dimensions and designs of experiments and create test-and-learn frameworks. Interpret data to identify trends to go across future data sets. Create continuous online model learning along with iterative model enhancements. Develop newer techniques (for example, advanced machine learning algorithms, auto ML) by leveraging the latest trends in machine learning/artificial intelligence to train algorithms to apply models to new data sets. Guide the team on feature engineering, experimentation, and advanced modeling techniques to be used for complex problems with unstructured and multiple data sets (for example, streaming data, raw text data).

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 to production, continuously log and track model behavior once it is deployed against the defined metrics. Identify model parameters which may need modifications depending on scale of deployment.

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.

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 analyze the business problem within one’s discipline and question assumptions to help the business identify the root cause. Identify and recommend approach to resolve the business problem to create effective technology-focused solutions. Set relevant deliverables based on the established success criteria and define key metrics to measure progress and effectiveness of the solution. Quantify business impact.

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 understand the priority order of requirements and service level agreements, define and identify the most suitable sources for required data that is fit for purpose, referring to external sources as required. Perform initial data quality checks on the extracted data. Review the deliverables of junior associates and provide guidance on data source and quality.

AI Agent Development: Requires knowledge and understanding of AI Agent, Multi Agent, and related technologies to be applied to both the development and enhancement of existing models and business processes.

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 the principles of the defined strategy to unique, moderately complex business problems that may span one or more functions or domains.

Drives the execution of multiple business plans and projects by identifying customer and operational needs, developing and communicating business plans and priorities, removing barriers and obstacles that impact performance, providing resources, identifying performance standards, measuring progress and adjusting performance accordingly, developing contingency plans and demonstrating adaptability, and supporting continuous learning.

Provides supervision and development opportunities for associates by selecting and training, mentoring, assigning duties, building a team-based work environment, establishing performance expectations and conducting regular performance evaluations, providing recognition and rewards, coaching for success and improvement, and promoting a belonging mindset in the workplace.

Promotes and supports company policies, procedures, mission, values, and standards of ethics and integrity by training and providing direction to others in their use and application, ensuring compliance with them, and utilizing and supporting the Open Door Policy.

Ensures business needs are being met by evaluating the ongoing effectiveness of current plans, programs, and initiatives; consulting with business partners, managers, coworkers, or other key stakeholders; soliciting, evaluating, and applying suggestions for improving efficiency and cost effectiveness; and participating in and supporting community outreach events.

Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.

Option 1: Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 5 years’ experience in an analytics related field. Option 2: Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 3 years’ experience in an analytics related field. Option 3: 7 years’ experience in an analytics or related field.

Preferred Qualifications…

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), We value candidates with a background in creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and integrating digital accessibility seamlessly. The ideal candidate would have knowledge of accessibility best practices and join us as we continue to create accessible products and services following Walmart’s accessibility standards and guidelines for supporting an inclusive culture.

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.

Walmart and its subsidiaries are committed to maintaining a drug-free workplace and has a no tolerance policy regarding the use of illegal drugs and alcohol on the job. This policy applies to all employees and aims to create a safe and productive work environment.