Product Quality Data Analyst-Hybrid

Elyon International•Bothell, WA

12h•Hybrid

About The Position

This role involves analyzing complex datasets using advanced statistical methods and machine learning techniques to provide insights that drive strategic business decisions and operational efficiencies. The analyst will create predictive models and visualizations to forecast trends and anomalies, present findings to stakeholders, and collaborate with cross-functional teams to implement data science solutions.

Requirements

Bachelor's or Master's Degree in Information Management, Information Systems, Computer Science, Data Analytics, or equivalent.
Minimum 5+ years of experience with a Bachelor's degree in areas such as Data Analytics, Data Modeling, or equivalent OR no prior experience required with a Master's Degree.

Nice To Haves

Data Analysis & Interpretation
Data Harmonization & Processing
Data Modeling
Data Quality Assurance
Statistical Methods
Statistical Programming Software
Business Intelligence Tools
Data Mining
Machine Learning
Engineering Fundamentals
Research & Analysis
Structured Query Language (SQL)
Regulatory Compliance

Responsibilities

Handles analysis of complex datasets using advanced statistical methods and machine learning techniques to uncover deep insights that inform strategic business decisions and operational efficiencies.
Creates sophisticated predictive models and data visualizations, utilizing tools like Python, R, and Palantir, to forecast trends, patterns, and anomalies for proactive decision-making, working under limited supervision.
Develops, documents, and disseminates comprehensive methodologies, processes, and analytical findings, ensuring transparency, reproducibility, and facilitating cross-functional knowledge sharing and collaboration.
Presents complex data-driven insights and strategic recommendations to senior stakeholders, effectively translating intricate analytical results into clear, actionable business strategies and influencing key decision-making processes.
Monitors, maintains, and continuously improves the performance of deployed models, conducting regular reviews, updates, and optimizations to ensure sustained accuracy, relevance, and business impact.
Interacts and collaborates with cross-functional teams, including IT, data engineering, and various business units, to ensure the successful operationalization, integration, and scaling of data science solutions across the organization.