Principal Data Scientist

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What you'll do...

Position: Principal Data Scientist 

Job Location: 702 SW 8th Street, Bentonville, AR 72712 

Duties: Tech. Problem Formulation: analyze the business problem within one's discipline and questions 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. Understanding Business Context: evaluate proposed business cases for projects and initiatives. Translate business requirements into strategies, initiatives, and projects and align 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 Source Identification: 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 provides guidance on data source and quality. Analytical Modelling: select appropriate modelling 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 (e.g., 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 modelling techniques to be used for complex problems with unstructured and multiple data sets (for example, streaming data, raw text data). Model Assessment and Validation: 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: 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. Code Development and Testing: write code to develop the required solution and application features by determining the appropriate programming language and leveraging business, t