Job Description

What you'll do... Position: Senior Data Scientist Job Location: 805 Moberly Ln, Bentonville, AR 72716 Duties: Understands, articulates, and applies principles of the defined strategy to routine business problems that involve a single function. Supports the understanding of the priority order of requirements and service level agreements. Helps identify the most suitable source for data that is fit for purpose. Performs initial data quality checks on extracted data. 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. Identifies the model evaluation metrics. Applies best practice techniques for model testing and tuning to assess accuracy, fit, validity, and robustness for multi-stage models and model ensembles. 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. 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. Generates appropriate graphical representations of data and model outcomes. Understands 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. Presents to and influences the team and business audience using the appropriate frameworks and conveys clear messages through business and stakeholder understanding. Customize communication style based on stakeholder under guidance and leverages rational arguments. Minimum education and experience required: Bachelor’s degree or the equivalent in Statistics, Computer Science, or related field plus 3 years of experience in analytics or a related field; OR Master’s degree or the equivalent in Statistics, Computer Science, or related field plus 1 year of experience in analytics or a related field; OR 5 years of experience in analytics or a related field. Skills Required: Must have experience with: Building large and complex deep learning models (RNN, CNN, and Transformers) which solve business use cases using different types of data (image, text, and tabular data); Statistical and machine learning techniques, including regression, classification, and ensemble methods to provide valuable business insights from data; Sourcing, cleaning, manipulating, and analyzing large amounts of data using distributed computing like Hadoop, HIVE, Spark, and Big Query; Python, TensorFlow, Jax and Torch to implement customized deep learning models; Different machine learning and statistical libraries and packages like scikit-learn, stats models, prophet, NumPy and SciPy; Different time series forecasting techniques like ARIMA, SARIMA, Holt-Winters and Sequence to Sequence methods; unsupervised machine learning techniques like Clustering, Auto Encoders and latent-variable models; Coding in object-oriented programming languages (C++, JAVA and Python); Statistical testing techniques sample size determination, determining test and control groups and performance metrics to evaluate performance of machine learning models; Exploratory data analysis using matplotlib, seaborn packages in Python and ggplot in R; Writing efficient data extraction and transformation queries using SQL; Feature extraction, feature selection, and techniques to control feature explosion using regularization and kernel methods; Building classical machine learning models using tree-based techniques such as gradient boosting in Python; and Building deep learning models on a combination of textual and tabular data using PyTorch and keras. Employer will accept any amount of experience with the required skills. #LI-DNP #LI-DNI Wal-Mart is an Equal Opportunity Employer.