Staff Data Scientist

Walmart•Bentonville, AR

3d•$132,621 - $220,000•Onsite

About The Position

The Staff Data Scientist will apply knowledge of analytics, big data analytics, automation techniques, business requirements, and relevant precedents to translate and co-own business problems within the discipline as data-related or mathematical solutions. They will identify appropriate methods and tools to address these problems and share use cases and examples that demonstrate how selected methods solve the business need. This role involves providing recommendations to business stakeholders to address complex business issues, developing business cases that reflect projected return on investment or cost savings, and translating business requirements into projects, activities, and tasks aligned to overall business strategy. The Staff Data Scientist will develop domain-specific artifacts, serve as a conduit between business needs and tangible solutions, and identify and recommend relevant insights related to the area of work. They will support the understanding of requirement prioritization and service-level agreements, help identify suitable data sources that meet fitness-for-purpose criteria, and perform initial data-quality checks on extracted data. This role requires selecting appropriate analytical modeling techniques for complex problems involving large, structured, and unstructured datasets, collaborating with the business to iteratively select and develop variables and features, conducting exploratory data analysis, identifying experiment dimensions, and designing test-and-learn frameworks. The Staff Data Scientist will interpret data to identify trends, create continuous online model-learning processes, develop newer techniques such as advanced machine-learning algorithms and AutoML, train algorithms, and guide teams on feature engineering, experimentation, and advanced modeling. They will identify model-evaluation metrics, apply best-practice model-testing and tuning techniques, deploy models to production, continuously log and track deployed-model behavior, and identify model parameters requiring modification. The role also includes writing code, creating test cases, developing proofs of concept, testing code, deploying software, contributing documentation, and providing timely progress updates. Additionally, the Staff Data Scientist will generate clear graphical representations of data and model outcomes, understand customer requirements to design effective representations, collaborate with UX designers and UI engineers, present and influence audiences using appropriate visualization frameworks, and guide and mentor junior associates.

Requirements

Master’s degree or the equivalent in Business Analytics, Statistics, Economics or related field plus 2 years of experience in data analytics or related field; OR Bachelor's degree or the equivalent in Business Analytics, Statistics, Economics or related field plus 4 years of experience in data analytics or related field.
Utilizing Python and R programming to perform Supervised Learning, Unsupervised Learning, Natural Language Processing and Time Series Forecasting
Implementing Anomaly Detection, Data munging & Imputation, Statistical Hypothesis Testing to detect and identify trends and anomalies in data
Using SQL for data querying and analysis
Performing Unit Testing of code and GitHub Code version management for reliable development of ML solution
Designing and implementing Convolutional Neural Networks, Deep Learning (TensorFlow, Keras and PyTorch), Ensemble Methods, and Bayesian Inference
Leveraging Big Data Technologies for large scale datasets and solving optimization problems using Linear & Integer Programming
Applying Regularization and Hyperparameter Tuning for model improvements
Performing Cross-Validation for developing robust models with a focus on Model explainability
Applying Data Compression techniques to reduce dimensionality for model interpretability
Using PySpark to process large datasets in distributed computing environments
Working with Google BigQuery, Microsoft Azure Databricks to store, process and analyze large volumes of structured and unstructured data in cloud
Utilizing Tableau and Microsoft Power BI for effective presentation of insights from data
Leveraging SAP BW, SAP BOBJ, SAP Lumira, SAP Analytics Cloud, Analysis for Office, SAP Bex Explorer and Alteryx to support enterprise reporting and analytics workflows
Working with Teradata, SAP HANA, NoSQL, PostgreSQL to design, query relational and non-relational databases
Implementing CI/CD integration and MLOps (mlflow) practices for experiment tracking and model life cycle management
Operating within Agile (Scrum and Kanban) frameworks to deliver solutions

Responsibilities

Apply knowledge of analytics, big data analytics, automation techniques, business requirements, and relevant precedents to translate and co-own business problems within the discipline as data-related or mathematical solutions.
Identify appropriate methods and tools to address those problems, and share use cases and examples that demonstrate how selected methods solve the business need.
Provide recommendations to business stakeholders to address complex business issues, develop business cases that reflect projected return on investment or cost savings.
Translate business requirements into projects, activities, and tasks aligned to overall business strategy.
Develop domain-specific artifacts, serve as a conduit between business needs and tangible solutions, and identify and recommend relevant insights related to the area of work.
Support understanding of requirement prioritization and service-level agreements, help identify suitable data sources that meet fitness-for-purpose criteria, and perform initial data-quality checks on extracted data.
Select appropriate analytical modeling techniques for complex problems involving large, structured, and unstructured datasets.
Collaborate with the business to iteratively select and develop variables and features based on model responses.
Conduct exploratory data analysis including basic statistical analysis, hypothesis testing, and statistical inference.
Identify experiment dimensions and design test-and-learn frameworks.
Interpret data to identify trends across future datasets.
Create continuous online model-learning processes with iterative enhancements.
Develop newer techniques such as advanced machine-learning algorithms and AutoML.
Train algorithms to apply models to new datasets.
Guide teams on feature engineering, experimentation, and advanced modeling for complex cases involving multiple or unstructured datasets.
Identify model-evaluation metrics, apply best-practice model-testing and tuning techniques to assess accuracy, fit, validity, and robustness for multi-stage models and model ensembles.
Deploy models to production, continuously log and track deployed-model behavior against defined metrics, and identify model parameters requiring modification based on deployment scale.
Write code using appropriate programming languages while leveraging business, technical, and data requirements.
Create test cases to validate proposed solution designs.
Develop proofs of concept, test code using suitable testing approaches, deploy software to production servers, contribute documentation and playbooks, and provide timely progress updates.
Generate clear graphical representations of data and model outcomes.
Understand customer requirements to design effective representations for multiple datasets.
Collaborate with UX designers and UI engineers to build front-end applications.
Present and influence audiences using appropriate visualization frameworks.
Convey clear messages grounded in business and stakeholder understanding, tailor communication style appropriately, and guide and mentor junior associates on story types.