• Analyze large and complex datasets to identify trends, patterns, and actionable insights that support business decision-making. • Develop, train, and validate statistical and machine learning models to solve business problems and improve operational performance. • Apply advanced analytics techniques such as predictive modeling, classification, clustering, and forecasting. • Collaborate with cross-functional teams to understand business requirements and translate them into data-driven solutions. • Perform data exploration, cleaning, and feature engineering to prepare datasets for modeling and analysis. • Design and conduct experiments and A/B tests to evaluate model performance and business impact. • Communicate insights and model results to technical and non-technical stakeholders through reports, visualizations, and presentations. • Work with data engineers and ML engineers to deploy models and integrate analytical solutions into production environments. • Develop dashboards and data visualizations to support business intelligence and reporting needs. • Ensure data quality, integrity, and proper documentation of analytical processes.

Reporting to the Manager Data Science, you will lead analytics projects within defined areas of expertise by researching, collecting, analyzing, and interpreting data to identify trends and deliver insights with actionable recommendations. You will define information requirements and collaborate with technology partners to design and implement dashboards, automated solutions, and digital factory data products, including Digital Yield intelligence and engineering Center of Excellence enablement, to support Project Catalyst programs and advance a self‑service analytics culture. A Taste of Your Responsibilities - Lead Digital Yield intelligence initiatives, including the development and enablement of digital factory data products and the engineering Center of Excellence, to support Project Catalyst programs - Lead the creation and continuous improvement of analyses, reports, insights, dashboards, and future agent‑driven capabilities enabled by artificial intelligence for internal stakeholders and external customers - Lead complex data requests and analytics projects in partnership with the broader analytics and data science teams - Develop and deliver advanced reports, analyses, and dashboards that support informed decision‑making - Drive business integration and adoption of analytics tools, insights, and data products - Provide recommendations on analytical approaches, methodologies, and solution design - Collaborate across functions to elevate the visibility, impact, and brand of the analytics and data science teams across the enterprise Ingredients Required for Your Success - Bachelor’s degree - Three or more years of experience in a data science or advanced analytics function, with Consumer Packaged Goods experience preferred - Experience delivering actionable business recommendations and partnering with cross‑functional stakeholders - Demonstrated track record of learning and applying modern analytics tools, technologies, and frameworks - Familiarity with a range of analytical and statistical models #LI‑Remote #LI‑MSL #LI-PM2 Compensation Pay Range:$81,000-$118,000 The annual salary listed above is the expected offering for this position. An employee’s actual annual salary will be based on but not limited to: location, relevant experience/level and skillset, while balancing internal Conagra employees’ equity. Conagra Brands will comply with applicable law regarding minimum salaries for exempt employees. Our Benefits We care about your total well-being and will support you with the following, subject to your location and role: - Health: Comprehensive healthcare plans, wellness incentive program, mental wellbeing support and fitness reimbursement - Wealth: Great pay, bonus incentive opportunity, matching 401(k) and stock purchase plan - Growth: Career development opportunities, employee resource groups, on-demand learning and tuition reimbursement - Balance: Paid-time off, parental leave, flexible work-schedules (subject to your location and role) and volunteer opportunities Our Company At Conagra Brands, we have a rich heritage of making great food. We aspire to have the most impactful, energized and inclusive culture in food. As a member of our 18,000+ person team across 40+ locations, you are empowered to reach your potential, make an impact and own your career. We're in the business of building champions – within our people and our iconic brands like Birds Eye®, Slim Jim® and Reddi-Wip®. Our focus on innovation extends beyond making great food, it also reflects our commitment to embracing new solutions that positively impact our team, the communities we serve and the health of our planet. Foodies Welcome. Conagra Brands is an equal opportunity employer and considers qualified applicants for employment without regard to sex, race, color, religion, ethnic or national origin, gender, sexual orientation, gender identity or expression, age, pregnancy, leave status, disability, veteran status, genetic information and/or any other characteristic or status protected by national, federal, state or local law. Reasonable accommodation may be made upon request.

• Design and refine advanced prompts for GitHub Copilot and LLM-based workflows • Build AI agents and automation workflows to accelerate development tasks • Optimise context window management, plan mode vs agent mode usage, and prompt structuring • Collaborate directly with Microsoft AI engineers to enhance tooling capabilities • Rapidly prototype and iterate AI-driven SDLC accelerators • Embed within multiple Scrum teams to review AI-generated code and specifications • Challenge and improve prompt usage and drive best practice adoption • Coach engineers in effective AI-assisted development • Identify gaps in AI outputs and improve workflows accordingly • Influence cultural change in AI adoption across global teams

• Design, build, and deploy machine learning solutions for audience targeting, lookalike generation, and individual propensity scoring. • Own the complete ML lifecycle - from exploratory analysis and experimentation all the way through production deployment and operational monitoring. • Develop and ship production ML systems spanning self-supervised representation learning, vector similarity search, and supervised classifiers. • Leverage distributed computing (Spark/Databricks) and cloud data platforms (AWS, Snowflake) to build and run production ML pipelines at scale. • Ensure model quality through rigorous evaluation practices: from embedding validation and retrieval quality to supervised model calibration and production monitoring. • Engineer features at scale from demographic, behavioral, and identity data — including handling missing values, encoding strategies, and pipeline-level data quality validation. • Contribute ML logic directly into shared production services, working alongside data engineering, software engineering, and product teams.