Job ID: [[id]]
Alternate Locations: [[mfield1]]
Newell Brands is a leading consumer products company with a portfolio of iconic brands like Graco®, Coleman®, Oster®, Rubbermaid®, Sharpie® and Yankee Candle® - and 24,000 talented teammates around the world. Our culture is built on values in action: Integrity, Teamwork, Passion for Winning, Ownership, and Leadership. We work together to win, grow, and make a real impact - supported by a high-performing, inclusive, and collaborative environment where you can be your best, every day.
We are building AI-enabled capabilities that help our marketing, eCommerce and content teams work faster and smarter, starting with content optimization and extending to other needs as they come up. The Forward Deployed Engineer designs and builds the solutions. You will work directly with the teams who own content, product data, creative, syndication and other marketing and eCommerce workflows, learn how their work gets done and build with them. This role is focused on building, and you will report to the Head of Digital Product & Innovation, eCommerce, who leads product strategy and stakeholder alignment. You will work closely with your leader on the product vision, using what you learn in the field to inform what we build next and why. What you build in the field informs what gets productized, and what gets productized is then deployed back into the business. This is a hands-on, high-visibility role.
Responsibilities
Discover and Design
- Work with New Item Setup, Marketing, Brand, eCommerce, Content, Media, Commercial and IT teams to map how work gets done and find where data issues, handoffs and rework slow teams down
- Take on new marketing and eCommerce needs as they come up, from workflow automation to analytics and reporting, and help prioritize them alongside ongoing work
- Find the underlying problem behind each request, and turn business requirements into scoped technical solutions with clear specifications and success metrics
- Contribute to architecture choices for each AI solution, including models, orchestration and data design, in partnership with enterprise IT, and recommend a different approach when the request is not the best fit
Build and Deploy
- Build and own technical components from design through deployment, testing and monitoring in production
- Rapidly prototype and deploy solutions such as AI agents, workflow automations, integrations, data pipelines and lightweight applications, then take them from prototype to production to meet enterprise standards
- Build and deploy AI agents and workflows that take action in business systems, including prompts, tool calls, retrieval and context layers
- Develop classification and enrichment models, such as those that map, validate and generate product attributes and categories against our taxonomy and retailer requirements
- Train, tune and deploy machine learning models that extract and classify information from text, images and documents, such as product attributes against our taxonomy, at scale
- Choose the right approach for each use case, from rules and classical models to fine-tuned and LLM-based extraction, and design feedback loops that retrain models using human corrections
- Connect copy and generative AI content capabilities into connected workflows, using a blend of AI within custom Azure solutions and enterprise tools such as Adobe and Salsify
- Build data pipelines and integrations across Azure, enterprise data platforms and the commercialization stack: product information management (PIM), syndication and digital asset platforms (Stibo, Salsify, Adobe)
- Work with enterprise IT to meet requirements for security, access control and data governance, and to integrate with legacy systems
- Use Claude Code and similar AI-assisted development tools daily to build and test faster
Operate and Improve
- Test AI output with defined test sets, quality metrics and human review
- Measure extraction and classification performance by attribute using precision, recall and F1 on labeled datasets, and track confidence thresholds, error patterns and model drift over time
- Monitor deployed solutions, find the root cause when issues occur and fix them
- Build dashboards that report solution health and business results: cycle time, data quality, cost avoidance and content readiness
- Help measure and report impact in terms stakeholders and finance care about, such as time saved, cost avoided, revenue influenced and quality improved
- Document solutions and train business teams to use and extend them independently, gathering feedback and iterating until the solution is part of how the team works
Inform the Product
- Contribute to the product vision and roadmap alongside your leader, combining what you learn in the field with their strategy and priorities
- Turn repeated field needs into reusable capabilities, and help decide what to build once, what to configure and what not to build
- Bring data, working demos and clear technical recommendations to stakeholder discussions to help reach decisions
- Keep stakeholders informed by communicating progress, tradeoffs and risks clearly to both working teams and senior leaders
- Stay current on new AI tools and methods, and recommend when to adopt, build or retire a capability
Qualifications
- Bachelor's degree in computer science, engineering, data science or a related field, or equivalent practical experience
- Five or more years in software engineering, data engineering, solutions engineering or a similar role such as forward-deployed engineering or consulting, including technical delivery for customers or business teams
- Strong Python and SQL skills
- Software engineering fundamentals, including version control, code review and automated testing
- Experience with Azure and DevOps practices, including CI/CD, infrastructure as code and cloud deployment
- Experience in data science, including feature engineering, model selection, validation and statistical evaluation, and moving models from experiment to production
- Hands-on machine learning for information extraction and classification, such as named-entity recognition, multi-label and hierarchical classification, embeddings, and fine-tuning of transformer or large language models
- Experience building with large language models, agent frameworks and orchestration, prompt and context design, evaluation and monitoring
- Experience with classification, taxonomy mapping or entity-resolution models
- Working knowledge of current AI tools and methods, with examples of what you have built or tested
Newell Brands (NASDAQ: NWL) is a leading global consumer goods company with a strong portfolio of well-known brands, including Rubbermaid, Sharpie, Graco, Coleman, Rubbermaid Commercial Products, Yankee Candle, Paper Mate, FoodSaver, Dymo, EXPO, Elmer’s, Oster, NUK, Spontex and Campingaz. We are focused on delighting consumers by lighting up everyday moments. Newell Brands and its subsidiaries are Equal Opportunity Employers and comply with applicable employment laws. EOE/M/F/Vet/Disabled are encouraged to apply.