Walmart-posted 3 months ago
$139,506 - $264,000/Yr
Full-time • Mid Level
Hoboken, NJ
General Merchandise Retailers

As a Staff Data Scientist, you will be responsible for translating and co-owning business problems within your discipline to data-related or mathematical solutions. You will identify appropriate methods and tools to provide solutions for complex business issues and share use cases to demonstrate how these methods can solve business problems. Your role will involve providing recommendations to business stakeholders, developing business cases for projects, and translating business requirements into actionable projects that align with the overall business strategy. You will serve as a conduit to connect business needs with tangible solutions, support the understanding of priority requirements, and help identify suitable data sources. Additionally, you will perform data quality checks, select modeling techniques, conduct exploratory data analysis, and create test frameworks. You will also be responsible for deploying models to production, tracking model behavior, and modifying parameters as needed. Your work will involve writing code, creating test cases, and generating graphical representations of data. You will collaborate with User Experience designers and User Interface engineers to build front-end applications and present findings to stakeholders.

  • Translate and co-own business problems to data-related solutions.
  • Identify appropriate methods and tools for problem-solving.
  • Provide recommendations to business stakeholders.
  • Develop business cases for projects with projected ROI.
  • Translate business requirements into actionable projects.
  • Serve as a conduit between business needs and solutions.
  • Support understanding of priority requirements and service level agreements.
  • Perform initial data quality checks on extracted data.
  • Select appropriate modeling techniques for complex problems.
  • Conduct exploratory data analysis on available data.
  • Create test and learn frameworks.
  • Develop newer techniques leveraging machine learning and AI.
  • Guide the team on feature engineering and advanced modeling techniques.
  • Deploy models to production and track their behavior.
  • Write code to develop required solutions and application features.
  • Create test cases and proofs of concept.
  • Generate graphical representations of data and model outcomes.
  • Collaborate with UX/UI teams to build front-end applications.
  • Present findings using appropriate data visualization frameworks.
  • Master's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, Engineering, or related field and 2 years of experience in an analytics-related field; OR Bachelor's degree in the same fields and 4 years of experience.
  • Experience developing and optimizing Machine Learning Models.
  • Experience designing and implementing deep learning models using PyTorch and TensorFlow.
  • Experience optimizing model performance through feature selection and hyperparameter tuning.
  • Experience conducting search and recommendation experiments and applying A/B testing.
  • Experience with Natural Language Processing (NLP) and Query Understanding.
  • Experience deploying machine learning models on cloud platforms like GCP and AWS.
  • Experience using Docker and Kubernetes for inference pipelines.
  • Experience processing large-scale data using PySpark and Hadoop.
  • Experience with NoSQL databases (MongoDB) and relational databases (MySQL).
  • Experience with Data Engineering and ETL Pipelines.
  • Competitive pay and performance-based incentive awards.
  • Health benefits including medical, vision, and dental coverage.
  • 401(k) and stock purchase options.
  • Company-paid life insurance.
  • Paid time off including sick leave, parental leave, and bereavement.
  • Short-term and long-term disability benefits.
  • Education assistance with 100% company-paid college degrees.
  • Company discounts and military service pay.
  • Adoption expense reimbursement.
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