Job Description

• This is an entry-level to early-career role for someone who is excited about the intersection of business problems and AI/ML capabilities. You dont need to be an expert you need to be genuinely curious about how AI is changing whats possible, comfortable working with data, and able to think critically about where intelligent automation makes sense (and where it doesnt).
• Youll work alongside our Data Engineer, who handles the technical implementation of pipelines and infrastructure. Your focus is on the "what" and "why" understanding business needs, identifying opportunities for intelligent services, designing how we should measure success, and building the analytical foundation that makes AI/ML initiatives possible. When you identify a promising AI opportunity, our development team brings it to life. Traditional reporting and dashboards are part of this work, but theyre a means to an end: enabling our products to get smarter over time.

• Your Top Outcomes (First 6-12 Months)
• Learn the Business & Technology Landscape: Develop working knowledge of Merkles operational workflows, key metrics, data systems (Salesforce, Workday, Dynamics 365, Power Platform), and current AI/ML capabilities in our tech stack
• Identify Intelligence Opportunities: 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
• Deliver Foundational Insights: Produce analyses that answer real business questions and influence the data foundations needed for future intelligent services
• Design Requirements for Intelligent Features: Partner with stakeholders and the Data Engineer to define requirements for at least 2 AI-enhanced capabilities, from problem definition through success metrics
• Build AI Evaluation Practices: Develop frameworks for how we assess AI/ML opportunities feasibility, data readiness, expected value, build vs. buy considerations
• Grow Your Skills: 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

• Key Responsibilities
• AI/ML Opportunity Discovery: Continuously evaluate operational challenges through an AI lens where could prediction, automation, or intelligent recommendations add value? Research whats possible with current AI/ML/LLM capabilities and bring ideas to the team
• Business Analysis: Meet with operations stakeholders to understand their challenges; translate those into analytical questions and potential intelligent service concepts
• Data Exploration & Foundation: Query and explore operational datasets to find patterns and insights; assess data readiness for ML initiatives; document findings for technical and non-technical audiences
• Intelligence Requirements Definition: Write clear requirements for AI-enhanced features what problem they solve, what data they need, how well measure success, what could go wrong
• Reporting & Visualization: Define metrics and dashboards that support both current decision-making and future AI initiatives; create specifications for the Data Engineer to implement
• Insight Delivery: Present analysis findings and AI opportunity assessments to stakeholders; explain trade-offs, feasibility, and expected value in business terms
• AI Landscape Awareness: Stay current on AI/ML/LLM developments relevant to operational use cases; evaluate new tools, services, and capabilities; share learnings with the team
• Collaboration: Partner with the Data Engineer on implementation feasibility; work with Power Platform team on how intelligent features integrate with existing applications
• Continuous Learning: Actively build expertise in AI/ML concepts, prompt engineering, and intelligent automation patterns alongside foundational analytics skills

• Core Competencies (Observable Behaviors)
• AI-Forward Thinking: Naturally considers how AI/ML could apply to problems; stays curious about new capabilities; thinks critically about where AI adds value vs. where simpler solutions work
• Analytical Curiosity: Asks "why" and "what if" questions; digs into data to understand whats really happening; not satisfied with surface-level answers
• Clear Communication: Explains technical concepts (including AI capabilities and limitations) in plain language; creates visualizations that tell a story; writes documentation others can understand
• Business Orientation: Thinks about how analysis and AI connect to real decisions and outcomes; focuses on actionable value, not just interesting technology
• Learning Agility: Picks up new tools and concepts quickly; stays current in a fast-moving field; applies lessons from one area to another
• Attention to Detail: Validates data before drawing conclusions; thinks through edge cases and failure modes; documents assumptions and limitations
• Collaborative Mindset: Works well with technical and non-technical teammates; asks for help when needed; shares knowledge freely

• Must-Have Skills & Tools
• Educational Background: Bachelors degree in a quantitative field (Statistics, Mathematics, Economics, Engineering, Computer Science, Business Analytics, Data Science) or equivalent practical experience
• Analytical Foundation: Demonstrated ability to work with data through coursework, projects, internships, or self-study
• AI/ML Foundations: Genuine curiosity about artificial intelligence, machine learning, and LLMs you follow developments in this space, youve experimented with AI tools, you know how to manage prompts, and you think about how these tools could be applied
• Hands-on AI Experience: Personal projects, coursework, or competitions involving ML; experimentation with tools like ChatGPT, Copilot, or other AI assistants for real tasks
• SQL Basics: Ability to write queries to filter, join, and aggregate data (or strong willingness to learn quickly)
• Spreadsheet Proficiency: Comfortable with Excel or Google Sheets for data manipulation and basic analysis
• Communication Skills: Strong written and verbal English; able to explain analytical and AI concepts to non-technical audiences

• Nice-to-Have Skills
• Advanced AI/ML Knowledge: Deeper understanding of machine learning concepts types of problems (prediction, classification, clustering), how models learn, what makes a good use case
• BI Tools: Experience with Power BI, Tableau, or similar visualization tools
• Programming: Python or R for data analysis; bonus if youve used ML libraries (scikit-learn, etc.)
• Statistics: Coursework or self-study in statistics, probability, or quantitative methods
• Business Domain: Interest in or exposure to operational processes, business metrics, or enterprise systems

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