Appriss Retail

Data Scientist

Appriss Retail

contract

Posted on:

Origin:  • 🇺🇸 United States

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Salary

💰 $115,000 - $125,000 per year

Job Level

Mid-LevelSenior

Tech Stack

AWSAzureCloudGoogle Cloud PlatformGreenplumNumpyPandasPythonScikit-LearnSQL

About the role

  • Write, maintain, and optimize production-level Python and SQL code for data pipelines, MLOps workflows, and related systems.
  • Analyze structured and unstructured datasets to identify trends, patterns, and opportunities for improvement.
  • Design, implement, and maintain automated data ingestion, transformation, and validation pipelines.
  • Contribute to the design, testing, and deployment of predictive and prescriptive models.
  • Support deployment of pipelines and ML models, including standing up and managing relevant cloud infrastructure.
  • Collaborate with engineering, product, and business teams to translate requirements into scalable, code-driven solutions.
  • Apply rigorous statistical and software engineering best practices to ensure accuracy, reproducibility, and reliability.
  • Continuously evaluate and integrate tools, frameworks, and methods that improve efficiency, scalability, and maintainability.
  • Communicate results and recommendations clearly to both technical and non-technical audiences.
  • Adhere to data governance, security, and privacy standards.

Requirements

  • Master’s Degree in Computer Science, Data Science, Statistics, Mathematics, or related field (Bachelor’s degree with significant relevant experience considered). Proven track record of writing production-ready Python and SQL code. Familiarity with common data and ML libraries (e.g., dbt, pandas, NumPy, scikit-learn). Strong SQL skills and experience with large, complex datasets. Experience in end-to-end data project delivery—from code development to deployment. Familiarity with version control (Git) and collaborative coding workflows. Strong understanding of software engineering principles in a data science context. Experience with statistical modeling, machine learning, and A/B testing. Ability to communicate technical concepts clearly and effectively. Commitment to producing high-quality, maintainable, and scalable code.