Frontend Developer

International Paper•Atlanta, GA

2d•$83,000 - $121,000•Hybrid

About The Position

The Frontend Developer will be responsible for building and maintaining internal interfaces, integrating backend logic, and supporting user-facing tools that enable data-driven decision-making. This role focuses on frontend development, encompassing the full process from design to backend connection. The developer will also explore and implement advanced capabilities such as agent automation, recommendations, and closed-loop optimization, positioning the team to scale AI/analytics capabilities as the AI software stack matures. Additionally, the role involves developing and maintaining automated reporting, monitoring, and alerting solutions, supporting data collection, cleansing, transformation, and governance, and partnering with various teams to deliver analytics solutions. The developer will apply statistical analysis, machine learning, and data science techniques, evaluate and implement AI-enabled tools, and translate requirements into scalable analytical solutions. Identifying opportunities to automate manual processes and improve existing workflows, as well as supporting deployment, testing, documentation, and continuous improvement of analytics solutions are also key responsibilities.

Requirements

Resume
GitHub, portfolio, or equivalent examples of technical work; for hiring review purposes, submitted examples should not include AI-assisted builds.

Nice To Haves

Bachelor’s Degree in Computer Science, Software Engineering, Data Science, Engineering, or related field + 2 years of relevant experience; equivalent Master’s degree accepted; non-relevant bachelor’s degree requires 5+ years of equivalent hands-on experience.
Hands-on experience developing Analytics applications, dashboards, and operational reporting solutions.
Hands-on experience developing API integrations, data pipelines, and automated workflows.
Hands-on experience developing custom applications that solve manufacturing, operational, or business problems.
Hands-on experience with User-friendly UI/UX design, including clean visual layout, intuitive navigation, error-proofing, and practical usability for business and manufacturing users.
Experience working with SQL databases, APIs, process historians, manufacturing systems, and time-series data.
Experience working with Data transformation, validation, and business logic.
Experience working with Microsoft-based development environments and tools, preferably including Azure DevOps repositories, source control, and collaboration within a Microsoft enterprise ecosystem.
Experience working with End-to-end workflows (data ingestion → processing → analytics → user interface).
Demonstrated ability to develop user-facing analytics tools and applications.
Demonstrated ability to integrate applications with operational and business data sources.
Demonstrated ability to build solutions that support manufacturing, reliability, quality, or operational improvement initiatives.
Demonstrated ability to own projects from concept through deployment.

Responsibilities

Build and maintain internal interfaces with pre-approved software stack with contracted SaaS vendors.
Develop external vendor builds.
Accelerate AI adoption and standardize scalable AI solutions across manufacturing and business functions.
Develop analytics applications, dashboards, and operational reporting solutions used by manufacturing, reliability, quality, and engineering teams.
Build integrations between operational data sources, business systems, and analytical applications.
Support user-facing tools that enable data-driven decision making across mills and corporate functions.
Integrate APIs, data pipelines, and Machine Learning (ML) outputs to User Interface (UI).
Integrate process historian and manufacturing system data.
Perform data transformation, validation, and business logic supporting analytical workflows.
Develop predictive analytics and recommendation engines that support operational improvement initiatives.
Reduce engineering bottlenecks.
Improve speed to value.
Support backend development.
Focus on frontend development, full process including design and backend connection.
Explore and implement advanced capabilities (agent automation, recommendations, closed-loop optimization).
Position the team to scale AI/analytics capabilities as AI software stack matures rather than reacting later with limited capacity.
Develop and maintain automated reporting, monitoring, and alerting solutions.
Support collection, cleansing, transformation, and governance of manufacturing and operational data.
Partner with operations, process engineers, reliability engineers, maintenance teams, and business stakeholders to identify and deliver analytics solutions.
Apply statistical analysis, machine learning, and data science techniques to support process optimization, reliability improvement, quality enhancement, and operational performance initiatives.
Evaluate and implement AI-enabled tools while validating outputs and ensuring responsible business use.
Translate operational and business requirements into scalable analytical solutions.
Identify opportunities to automate manual processes and improve existing workflows.
Support deployment, testing, documentation, and continuous improvement of analytics solutions.