Lead Analyst

Job Description:

Operations Intelligence Analyst

Title: Operations Intelligence Analyst

Location: India (GDC) | Work Model: Hybrid |Team: Solutions & Innovation (CXM Americas Operations)

Reports to: Power Platform Technical Architect

Merkle Operations serves as the heart of internal excellence for our Americas region, partnering with Finance, HR, IT, and delivery teams to create solutions that make our teams more effective and efficient. Our Solution Development team builds Power Platform applications that digitize and automate operational workflows — and we're now on a mission to make those tools intelligent through AI, machine learning, and LLM-powered capabilities.

As our Operations Intelligence Analyst, you'll help us figure out where and how AI can accelerate our products' success. You'll look at our operational challenges and ask: "Could an ML model predict this? Could an LLM automate that? What data would we need to make our tools smarter?" You'll combine traditional analytics foundations with an eye toward intelligent services — identifying opportunities, defining requirements, and helping the team prioritize where AI adds real value.

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 don't need to be an expert — you need to be genuinely curious about how AI is changing what's possible, comfortable working with data, and able to think critically about where intelligent automation makes sense (and where it doesn't).

You'll 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 they're a means to an end: enabling our products to get smarter over time.

Learn the Business & Technology Landscape: 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

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

AI/ML Opportunity Discovery: Continuously evaluate operational challenges through an AI lens — where could prediction, automation, or intelligent recommendations add value? Research what's 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 we'll 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

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 what's 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

Educational Background: Bachelor's 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, you've 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

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 you've 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

Microsoft Ecosystem: Familiarity with Microsoft Fabric, Azure AI services, Microsoft 365 Copilot, or Power Platform

We welcome applicants who are early in their careers. If you've just graduated or have limited work experience, that's okay. We're looking for potential, not polish. Show us:

< >Projects where you worked with data or AI (academic, personal, Kaggle, hackathons)Evidence that you're excited about AI/ML — what you've read, built, or experimented withExamples of how you explained something complex in simple termsAny exposure to real business problems, even through case competitions or internshipsDaily partnership with Data Engineer on analysis and reporting projectsRegular interaction with operations teams to understand their data needsCoordination with Power Platform Architect on technical direction and integrationParticipation in team standups, sprint planning, and knowledge-sharing sessions 

< >Location: India GDC offices with hybrid flexibility (2-3 days in office)Hours: Standard India business hours with 1-2 hours evening overlap (typically 7:30-9:30 PM IST) for US collaboration 

< >Front-row seat to applying AI/ML to real business problems — not theoretical, but practicalOpportunity to shape how a global organization adopts intelligent servicesHands-on experience identifying, defining, and launching AI-powered capabilitiesMentorship from experienced architects and engineersDedicated learning budget for AI/ML training and certificationsClear growth path — from Analyst to Senior Analyst to AI Product Manager or Data Scientist rolesCollaborative team culture that values curiosity, experimentation, and learning

Merkle is committed to equitable hiring and growth. We use structured interviews and evaluation rubrics to reduce bias and ensure fair assessment. We welcome diverse perspectives and backgrounds, and provide accommodation as needed throughout the hiring process.

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