What you'll do...
Position: Senior Manager, Data Science
Job Location: 811 Excellence Dr, Bentonville, AR 72716
Duties: Tech. Problem Formulation: To 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: 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 aligns to overall business strategy and develops domain specific artifact. 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. Data Source Identification: 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 provides guidance on data source and quality. Analytical Modeling: Select and develop variables and features iteratively based on model responses in collaboration with the business. Conducts 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. Model Assessment and Validation: Apply best practice techniques for model testing and tuning to assess accuracy, fit, validity, and robustness for multi-stage models and model ensembles. Model Deployment and Scaling: 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: 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. Data Visualization: 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 conveys clear messages through business and stakeholder understanding. Customize communication style based on stakeholder under guidance, and leverages rational arguments. Data Strategy: 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.
Minimum education and experience required: Master's degree or the equivalent in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field plus 3 years of experience in analytics or related experience; OR Bachelor's degree or the equivalent in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field plus 5 years of post-bachelor's, progressively responsible experience in analytics or related experience.
Rate of pay: $132,621.00 - 220,000.00/year
Skills required: Must have experience with: Designing and implementing scalable machine learning models incorporating data pipelines, model training, inferencing, versioning and monitoring; Coding in an object-oriented programming language Python; Writing unit tests using pytest and unittest for Python; Designing and implementing APIS in Python using Fastapi; Developing, optimizing and deploying machine learning models using Pyspark and Azure Databricks; Extracting and preprocessing large datasets from Google Cloud Storage and Google Big Query for machine learning models; Implementing CI/CD pipelines using Jenkins and using Git for Source Control Management; Implementing platform for tracking data science experiments, managing models, and managing model versions using MLflow; Developing web applications using Python with Plotly Dash; Developing event-driven systems using Google Pub/Sub for message queuing and real time communication between services; Utilizing caching strategies to store high-volume static API response onto disk stores and Redis cache to improve the performance. Employer will accept any amount of experience with the required skills.
