Appriss Retail

Data Scientist

• 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.

We’re seeking a Senior Data Scientist and natural storyteller to work within the Data Science team, serving as a critical link between technical analytics output and client-facing communication.
Analyze consumer data and machine learning model decisions to uncover patterns, trends, and anomalies relevant to client business outcomes.
Work with large datasets using SQL to extract, clean, and analyze data to support custom analyses.
Build automated analyses using Python and SQL that can be shared internally and feed production reporting endpoints.
Partner with the Revenue Organization to create and deliver PowerPoint presentations that effectively communicate insights to non-technical stakeholders.
Collaborate with other members of the Data Science team to ensure analysis is technically accurate and aligned with business needs.
Translate complex machine learning concepts into understandable business narratives and visualizations.
Leverage software development best practices when working with data pipelines, code repositories, and version control.