Data Scientist

Avery Dennison•Mentor, OH

12d•$77,775 - $116,144•Hybrid

About The Position

Avery Dennison Corporation is seeking a Data Scientist to partner with IT, Operations, and Engineering to understand system architecture for Quality processes, identify data fragmentation issues, and develop data-driven applications. The role involves translating business requirements into technical specifications, building data pipelines, cleaning and modeling data, and performing exploratory data analysis and model validation. This position will also support training on new digital solutions.

Requirements

Bachelor’s or Master’s degree (preferred) in computer science, software engineering, business analytics, applied mathematics, statistics, machine learning, or a data-centric discipline.
4+ years of relevant industry experience in advanced analytics and software development.
Strong skills in multiple programming languages including Python, SQL, Javascript, HTML, and CSS, with experience writing complex queries and optimizing relational database performance.
Advanced analytics expertise in descriptive statistical modeling, machine learning algorithms, predictive analytics, and data visualization tools like Matplotlib, Tableau, Cognos, or Graphana.
Familiarity with cloud data ecosystems (GCP, AWS, Azure), workflow automation, Oracle ERP/Analytics business data reporting, or manufacturing execution systems (MES/SCADA/historian databases).

Responsibilities

Partner with IT, Operations, and Engineering to learn system architecture enabling Quality processes and identify key pain points with fragmented data and systems.
Work closely with a project manager to align on milestones and objectives, developing, deploying, and maintaining data-driven applications and services aligned with a strategic digital roadmap.
Translate Quality business requirements into clear technical specifications, directing relevant IT teams and resources to develop and deploy production-ready data pipelines.
Formulate data gathering methodologies, write SQL queries, and define data assets, models, and pipelines to clean, manipulate, and organize data for robust analytics and future AI deployments.
Use statistical techniques and visualization tools to discover patterns and insights, rigorously test models to ensure high-quality outputs, and support training on new digital solutions.