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About the role
- Operations Intelligence Analyst supporting the application of AI/ML to optimize business processes at Merkle. Collaborating with data teams and operational stakeholders to identify and implement intelligent solutions.
Responsibilities
- Develop working knowledge of Merkle's operational workflows, key metrics, data systems (Salesforce, Workday, Dynamics 365, Power Platform), and current AI/ML capabilities in our tech stack
- Research and propose 3-5 areas where AI/ML could improve operational outcomes — predictive models, LLM-powered automation, intelligent recommendations — with clear business cases for each
- Produce analyses that answer real business questions and influence the data foundations needed for future intelligent services
- Partner with stakeholders and the Data Engineer to define requirements for at least 2 AI-enhanced capabilities, from problem definition through success metrics
- Develop frameworks for how we assess AI/ML opportunities — feasibility, data readiness, expected value, build vs. buy considerations
- Complete training in Power BI, SQL, and foundational analytics; actively build knowledge of AI/ML concepts, LLM capabilities, and how to evaluate intelligent service opportunities
- Meet with operations stakeholders to understand their challenges; translate those into analytical questions and potential intelligent service concepts
- Define metrics and dashboards that support both current decision-making and future AI initiatives; create specifications for the Data Engineer to implement
- Present analysis findings and AI opportunity assessments to stakeholders; explain trade-offs, feasibility, and expected value in business terms
- Stay current on AI/ML/LLM developments relevant to operational use cases; evaluate new tools, services, and capabilities; share learnings with the team
- Partner with the Data Engineer on implementation feasibility; work with Power Platform team on how intelligent features integrate with existing applications
- Actively build expertise in AI/ML concepts, prompt engineering, and intelligent automation patterns alongside foundational analytics skills
Requirements
- Bachelor's degree in a quantitative field (Statistics, Mathematics, Economics, Engineering, Computer Science, Business Analytics, Data Science) or equivalent practical experience
- Demonstrated ability to work with data — through coursework, projects, internships, or self-study
- Genuine curiosity about artificial intelligence, machine learning, and LLMs — you follow developments in this space, you've experimented with AI tools, you know how to manage prompts, and you think about how these tools could be applied
- Personal projects, coursework, or competitions involving ML; experimentation with tools like ChatGPT, Copilot, or other AI assistants for real tasks
- Ability to write queries to filter, join, and aggregate data (or strong willingness to learn quickly)
- Comfortable with Excel or Google Sheets for data manipulation and basic analysis
- Strong written and verbal English; able to explain analytical and AI concepts to non-technical audiences.
Benefits
- Front-row seat to applying AI/ML to real business problems — not theoretical, but practical
- Opportunity to shape how a global organization adopts intelligent services
- Hands-on experience identifying, defining, and launching AI-powered capabilities
- Mentorship from experienced architects and engineers
- Dedicated learning budget for AI/ML training and certifications
- Clear growth path — from Analyst to Senior Analyst to AI Product Manager or Data Scientist roles
- Collaborative team culture that values curiosity, experimentation, and learning
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