Data Scientist III

Position: Data Scientist III 

Job Location: 10790 Parkridge Dr., Suite 200, Reston, VA 20191 

Duties: Applied Business Acumen: Supports the development of business cases and recommendations. Owns delivery of project activity and tasks assigned by others. Supports process updates and changes. Solves business issues. Data Source Identification: Understand the appropriate data set required to develop simple models by developing initial drafts. Supports the identification of the most suitable source for data. Maintains awareness of data quality. Data Strategy: Understands, articulates, and applies principles of the defined strategy to routine business problems that involve a single function. Analytical Modeling: Selects the analytical modeling technique most suitable for the structured, complex data and develops custom analytical models. Conducts exploratory data analysis activities (for example, basic statistical analysis, hypothesis testing, statistical inferences) on available data. Defines and finalizes features based on model responses and introduces new or revised features to enhance the analysis and outcomes. Identifies the dimensions of the experiment, finalizes the design, tests hypotheses, and conducts the experiment. Perform trend and cluster analysis on data to answer practical business problems and provide recommendations and key insights to the business. Mentors and guides junior associates on basic modeling and analytics techniques to solve complex problems. Model Deployment & Scaling: Supports efforts to ensure that analytical models and techniques used can be deployed into production. Supports evaluation of the analytical model. Supports the scalability and sustainability of analytical models. Code Development & Testing: Writes code to develop the required solution and application features by using the recommended programming language and leveraging business, technical, and data requirements. Test the code using the recommended testing approach. Problem Formulation: Translates business problems within one's discipline to data related or mathematical solutions. Identifies what methods (for example, analytics, big data analytics, automation) would provide a solution for the problem. Shares use cases and gives examples to demonstrate how the method would solve the business problem. Model Assessment & Validation: Supports model fit testing and statistical inferences to evaluate performance. Assesses the impact of variables and features on model performance. Data Visualization: Generates appropriate graphical representations of data and model outcomes under guidance. Supports the understanding of customer requirements and designs data representations for simple data sets. Presents to and influences the team using the appropriate frameworks and conveys messages through basic business understanding. Demonstrates up-to-date expertise and applies this to the development, execution, and improvement of action plans by providing expert advice and guidance to others in the application of information and best practices; supporting and aligning efforts to meet customer and business needs; and building commitment for perspectives and rationales.  

Minimum education and experience required: Master’s degree or equivalent in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field; OR Bachelor’s degree or equivalent in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 2 years of experience in analytics or related experience. 

Skills required: Must have experience with: Conducting data cleaning, data wrangling, and data analysis using a coding language such as Python; Processing and manipulating big data using tools like PySpark; Using Big Query/SQL language to query data, conduct analysis, and manage database; Building machine learning models such as XG Boost and Neural Network to make business predictions; Using visualization tools such as Tableau and Plotly to present data insights and track model performance; Using Git to maintain and version control code base; Applying natural language techniques (BERT, etc.) to analyze texts and generate text embeddings; Using cloud technologies (Googled Cloud, etc.) to develop, deploy, and maintain data science projects; Using Linux terminal commands to access and manage virtual machines; Applying unsupervised machine learning techniques including clustering and dimensional reduction; Defining key metrics to track performance of machine learning model; Evaluating Impact of variables and features on machine learning model performance; Machine learning model fit testing, tuning, and validation techniques; and Automating data pipelines and model training processes to handle large amount of data. Employer will accept any amount of graduate coursework, graduate research experience or experience with the required skills. 

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Wal-Mart is an Equal Opportunity Employer.