Data Quality Analyst

Roots Automation - New York City, NY

Full-time - Mid Level

New York City, NY

Publishing Industries

About the position

As our customers grow, maintaining the quality and consistency of the datasets that feed into our AI systems is crucial to our success. Our production processes generate vast amounts of data, which we use for model training and analytics. To maintain the accuracy, relevance, and high quality of the data powering our AI systems, we are seeking a Data Quality Analyst to join our team.

Responsibilities

• Leverage data analysis tools (Python, SQL, Jupyter) and visualization platforms (Tableau, Looker, etc.) to monitor data quality, identify trends, and present actionable insights to cross-functional teams.
• Perform exploratory data analysis (EDA) to identify inconsistencies, anomalies, or data quality issues that may impact model training.
• Collaborate with cross-functional teams (AI/ML, Product, Customer Success) to understand the data generated by their processes and how it affects our models.
• Work closely with Customer Success teams to understand customer workflows and how they translate into data. Use this knowledge to guide the identification and resolution of data quality issues.
• Guide model tutor teams to correct data issues, ensuring the continuous improvement of our data quality processes.
• Be accountable for maintaining the overall quality and reliability of data for all machine learning and analytics needs across the company.
• Collaborate with AI/ML teams to refine datasets and ensure that they are aligned with model requirements.
• Create reports on data quality metrics and improvements.

Requirements

• Experience in data analysis, data quality management, or a related field.
• Familiarity with data analysis tools and techniques, including Python, SQL, and at least one analytics platform such as Tableau, Looker, or similar software.
• Strong communication skills, with the ability to work collaboratively across teams and translate customer processes into data insights.
• Attention to detail and a proactive approach to problem-solving.
• Ability to manage multiple projects and work effectively in a fast-paced, dynamic environment.