Sr. Business Analyst- AI Prototyping & Automation

📍 Job Overview

Job Title: Sr. Business Analyst - AI Prototyping & Automation

Company: Jobgether (on behalf of a partner company)

Location: United States

Job Type: Full-time

Category: Revenue Operations / GTM Operations / Business Analysis (with AI/Automation focus)

Date Posted: June 02, 2026

Experience Level: Mid to Senior Level (5+ years)

Remote Status: Remote (US-based)

🚀 Role Summary

• Spearhead the identification and prioritization of high-impact AI-driven automation opportunities across the enterprise, managing a dynamic pipeline of potential projects.

• Develop robust business cases and ROI models to quantify the financial benefits and strategic value of AI automation initiatives before significant engineering investment.

• Translate complex, ambiguous business challenges into clearly defined AI/automation use cases with measurable success criteria, acting as a critical bridge between business needs and technical solutions.

• Build and demonstrate functional AI prototypes using modern AI tooling and agent-based workflows, validating feasibility and impact rapidly to accelerate decision-making.

• Drive enterprise transformation by proving the ROI and feasibility of AI solutions, directly influencing executive decisions and shaping automation priorities.

📝 Enhancement Note: This role is positioned at the nexus of traditional business analysis and cutting-edge AI/ML application. While the title is "Sr. Business Analyst," the core responsibilities and required skills heavily lean into GTM and Revenue Operations enablement through AI. The emphasis on "AI Prototyping & Automation" and "agent-based workflows" suggests a need for understanding how AI can optimize sales processes, customer engagement, and internal operational efficiency, aligning it with the broader GTM and RevOps ecosystem.

📈 Primary Responsibilities

• Proactively identify, evaluate, and prioritize AI automation opportunities by collaborating with diverse stakeholders across business units, leadership, and operational teams.

• Develop comprehensive business cases featuring detailed ROI modeling, including projections for cost savings, efficiency gains, revenue enhancement, and performance improvements.

• Define and scope AI/automation use cases, translating ambiguous business problems into actionable requirements with clearly defined, measurable success metrics.

• Design, build, and iterate on functional AI prototypes using contemporary AI tools and agent-based frameworks to rapidly validate feasibility, impact, and user experience.

• Present compelling demonstrations of prototype solutions to executive leadership and key stakeholders, providing clear data to support go/no-go decisions for scaled implementation.

• Facilitate seamless knowledge transfer and collaboration with engineering teams by providing well-documented requirements and validated prototype designs for efficient handover.

• Establish and maintain metrics for tracking the progress and impact of the AI automation portfolio, generating executive-level reports on performance, realized value, and strategic alignment.

📝 Enhancement Note: The responsibilities highlight a project lifecycle ownership, from ideation and validation to handover. For a GTM/RevOps context, this translates to identifying opportunities within the customer journey (e.g., lead qualification, sales forecasting, customer support automation), building prototypes to test these hypotheses, and then working with engineering to integrate successful solutions into the existing tech stack.

🎓 Skills & Qualifications

Education:

• Bachelor's degree in a relevant field such as Computer Science, Business Administration, Engineering, Data Science, or a related discipline. Advanced degrees or specialized certifications in AI/ML are a plus. Experience:

• A minimum of 5 years of progressive experience in business analysis, management consulting, product operations, or similar analytical roles, with a proven track record of driving impactful business initiatives.

• Demonstrable hands-on experience in applied AI, specifically working with Large Language Models (LLMs), including expertise in prompting, fine-tuning concepts, evaluation methodologies, and designing AI-driven workflows. Required Skills:

• Business Analysis & Strategy: Proven ability to conduct thorough business analysis, define strategic objectives, and translate them into actionable plans.

• AI/ML Proficiency: Hands-on experience with LLMs, including prompt engineering, evaluating model outputs, and designing AI-powered automation workflows.

• Programming & Data Analysis: Working proficiency in Python for scripting, data manipulation, and light automation tasks, along with strong SQL skills for data querying and extraction.

• Financial Acumen: Expertise in financial modeling, including robust ROI analysis, cost-benefit assessments, and sensitivity analysis to quantify business value.

• Communication & Presentation: Exceptional ability to articulate complex technical and business insights clearly and concisely to diverse audiences, including executive leadership and technical teams.

• AI Framework Familiarity: Experience or strong familiarity with modern AI/agent frameworks such as LangGraph, AutoGen, or Microsoft Cognitive Services (MCP), or equivalent platforms.

• Adaptability & Bias for Action: Demonstrated ability to thrive in ambiguous, fast-paced, and evolving environments, with a strong inclination towards experimentation and rapid execution.

Preferred Skills:

• Industry Experience: Background in regulated industries such as healthcare, financial services, or insurance, or experience in SaaS/technology sectors, is highly advantageous.

• Agentic Workflow Design: Deep understanding and practical experience with agent-based workflows and multi-agent systems for complex problem-solving.

• Product Management Principles: Familiarity with product management methodologies and the product development lifecycle.

• Data Visualization Tools: Experience with data visualization tools for reporting and executive dashboards.

📝 Enhancement Note: The emphasis on "AI Prototyping & Automation" and specific frameworks like LangGraph and AutoGen indicates a need for candidates who are not just analysts but also builders. The "5+ years" experience, combined with hands-on AI, suggests a senior individual contributor role capable of independent work and strategic influence. The role blends analytical rigor with practical, hands-on AI development.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• AI Prototype Demonstrations: Showcase examples of functional AI prototypes built to validate specific business hypotheses or automation use cases, detailing the problem addressed, the AI approach, and the outcome.

• Business Case & ROI Analysis: Include samples of structured business cases or ROI analyses developed for technology or process improvement initiatives, demonstrating financial modeling and analytical rigor.

• Workflow Design Documentation: Present examples of documented workflows, particularly those involving automation or complex process mapping, illustrating clarity, detail, and efficiency considerations.

• Cross-Functional Project Examples: Highlight projects where you acted as a liaison between business stakeholders and technical teams, demonstrating your ability to translate requirements and manage expectations.

Process Documentation:

• Use Case Definition: Demonstrate a clear framework for defining AI/automation use cases, including problem statements, success criteria, and potential risks.

• Prototyping Methodology: Detail your approach to rapid AI prototyping, including tool selection, iteration cycles, and validation techniques.

• Impact Measurement Frameworks: Provide examples of how you have designed and implemented frameworks for measuring the impact and ROI of implemented solutions, including key performance indicators (KPIs).

📝 Enhancement Note: For this AI-focused role, the portfolio should emphasize practical application of AI concepts. Candidates should be prepared to walk through their prototypes, explain the technical choices made, and articulate the business value derived. Demonstrating the ability to rapidly iterate and validate ideas is crucial.

💵 Compensation & Benefits

Salary Range:

• The provided salary range for this role is $95,000 - $144,000 USD annually. This range is competitive and aligns with industry benchmarks for a Senior Business Analyst with specialized AI/automation expertise in the US market, taking into account experience level, required technical skills, and the dynamic nature of AI roles. Benefits:

• Health & Wellness: Comprehensive medical, dental, and vision insurance plans.

• Financial Security: Life insurance and disability coverage.

• Work-Life Balance: Generous paid time off (18+ days annually) plus company holidays.

• Retirement Planning: 401(k) retirement plan with potential company matching.

• Flexibility: A flexible work environment with minimal travel expectations, supporting remote work.

• Professional Development: Opportunities for career growth and skill enhancement within an innovation-focused company.

• Inclusive Culture: Access to a collaborative culture that values learning, experimentation, and impactful contributions.

Working Hours:

• Standard full-time working hours, typically 40 hours per week. The flexible work environment suggests potential for some autonomy in scheduling, provided core responsibilities and stakeholder availability are met.

📝 Enhancement Note: The salary range is explicitly stated and appears to be based on market data for similar roles and experience levels in the US. The inclusion of specific benefits details is standard for attracting talent in the competitive tech and operations space.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology / AI / Automation Services (as Jobgether is a platform for tech roles, and the role itself is AI-focused).

Company Size: Not specified for the partner company, but Jobgether itself is a growing platform. The role implies a dynamic, potentially fast-paced environment typical of tech startups or innovative divisions within larger corporations.

Founded: Not specified for the partner company.

Team Structure:

• Operations Focus: The role is positioned within a function that supports broader GTM and operational efficiency through AI. It likely involves close collaboration with business leaders, product teams, and engineering.

• Reporting: Likely reports to a Director or VP level within a Strategy, Operations, or Innovation group.

• Collaboration: High degree of cross-functional collaboration with stakeholders who have business problems, and with engineering teams who build solutions. The "bridge between business stakeholders and engineering teams" is a key aspect.

Methodology:

• Agile & Iterative: The description emphasizes a "fast-paced, experimental, and highly iterative" environment, suggesting agile methodologies for prototyping and development.

• Data-Driven Decision Making: Strong focus on ROI modeling, clear success criteria, and impact measurement to inform executive decisions.

• Prototyping First: A core methodology is building functional prototypes to validate value before large-scale investment.

Company Website: https://jobgether.com/ (for the platform facilitating the application)

The partner company's website is not specified.

📝 Enhancement Note: The culture is described as experimental and iterative, which is common in AI and product development. For operations professionals, this means adapting to new tools, processes, and priorities quickly. The emphasis on "proving ROI and feasibility before large-scale investment" is a critical operational checkpoint.

📈 Career & Growth Analysis

Operations Career Level: This is a Senior Individual Contributor role. It requires significant analytical depth, hands-on technical skills in AI, and the ability to influence strategic decisions. It's a step beyond traditional Business Analyst roles, integrating product thinking and AI execution.

Reporting Structure: Expected to report to a senior leader (Director/VP) responsible for innovation, strategy, or operational excellence. The role requires significant autonomy and direct interaction with executive stakeholders.

Operations Impact: The impact is direct and significant, focusing on accelerating enterprise-wide transformation through AI-driven automation. Success in this role directly influences strategic investment decisions, operational efficiency, and potentially revenue generation through optimized GTM processes. The ability to "prove ROI and feasibility before large-scale investment" is a critical lever for operations and business scaling.

Growth Opportunities:

• Specialization: Deepen expertise in specific AI domains, agent frameworks, or industry applications of AI automation.

• Leadership: Transition into AI product management, lead AI strategy initiatives, or manage teams focused on AI implementation and operations.

• Strategic Influence: Become a key advisor to executive leadership on AI strategy and digital transformation.

• Technical Advancement: Gain experience with a wider array of AI tools, MLOps practices, and advanced AI architectures.

📝 Enhancement Note: This role offers a unique blend of analytical and technical growth. For operations professionals, it's an opportunity to evolve their skill set into high-demand AI and automation areas, directly impacting business strategy and execution.

🌐 Work Environment

Office Type: The role is described as remote-friendly within the United States. This implies a distributed or hybrid work model, with a strong emphasis on digital collaboration tools.

Office Location(s): Primarily remote within the United States. Specific company office locations for the partner are not detailed, but the expectation is that the role can be performed remotely.

Workspace Context:

• Digital Collaboration: Expect heavy reliance on virtual communication tools (e.g., Slack, Zoom, Teams) for team and stakeholder interactions.

• AI Tools & Technology: Access to and proficiency with modern AI platforms, LLM APIs, and potentially cloud-based development environments will be essential.

• Cross-functional Interaction: Opportunities for regular interaction with diverse teams, including business leaders, product managers, data scientists, and software engineers.

Work Schedule:

• While typically 40 hours per week, the "flexible work environment" suggests some autonomy in structuring the workday. However, given the collaborative nature and the need to interact with stakeholders and engineering teams, availability during core business hours for the relevant time zones will be expected. The iterative and experimental nature may also require periods of focused work or rapid iteration.

📝 Enhancement Note: The remote nature of the role requires strong self-discipline, excellent asynchronous communication skills, and proficiency with virtual collaboration tools. Understanding how to foster collaboration and drive projects forward in a distributed environment is key.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Likely involves an AI-powered matching process by Jobgether, followed by a human recruiter screen to assess basic qualifications and fit.

• Technical/Analytical Interview: Focuses on business analysis skills, problem-solving, and understanding of AI concepts. May involve case studies related to ROI modeling or use case definition.

• AI Prototyping/Technical Deep Dive: A session to discuss specific AI tools, LLM experience, Python/SQL skills, and potentially a hands-on coding or prototyping exercise.

• Stakeholder/Leadership Interview: Assesses communication, presentation skills, ability to influence, and strategic thinking. This is where portfolio presentations are often key.

• Final Round: May involve interviews with senior leadership to evaluate cultural fit, long-term potential, and strategic alignment.

Portfolio Review Tips:

• Showcase AI Prototyping: Be prepared to demonstrate 1-2 functional AI prototypes. Explain the business problem, the AI approach (LLM, agent framework, etc.), the development process, and the validated outcomes or learnings.

• Quantify Impact: For all portfolio pieces, emphasize measurable results. Use metrics for ROI, efficiency gains, cost savings, or performance improvements. Clearly articulate the "value" demonstrated.

• Structure Your Narrative: For case studies, follow a clear structure: Problem -> Solution (your approach) -> Results -> Learnings. Tailor this to AI prototyping and business analysis.

• Highlight Collaboration: Provide examples of how you worked with stakeholders to define requirements, gather feedback, and ensure the solution met business needs.

• Tool Proficiency Emphasis: Be ready to discuss the specific AI tools, LLMs, programming languages, and frameworks used in your portfolio projects.

Challenge Preparation:

• AI Use Case Identification: Practice identifying and scoping potential AI automation opportunities within typical business functions (e.g., sales, customer service, finance).

• ROI Calculation: Be ready to perform quick ROI calculations or discuss the methodology for complex financial assessments.

• Technical Explanation: Prepare to explain technical AI concepts (e.g., LLM prompting, agent workflows) in simple, business-friendly terms.

• Problem Decomposition: Practice breaking down ambiguous business problems into smaller, manageable components that can be addressed with AI or automation.

📝 Enhancement Note: The application process is likely to be rigorous, with a heavy emphasis on practical demonstration of skills through a portfolio. Candidates should prepare to not only talk about their experience but also show concrete examples of their work, especially in AI prototyping and business case development.

🛠 Tools & Technology Stack

Primary Tools:

• AI/ML Platforms: Experience with LLM APIs (e.g., OpenAI, Anthropic, Google AI), AI model evaluation frameworks, and potentially cloud AI services (e.g., AWS SageMaker, Azure ML).

• AI Agent Frameworks: Familiarity with LangGraph, AutoGen, Microsoft Cognitive Services (MCP), or similar tools for building multi-agent systems and complex workflows.

• Programming Languages: Python (essential for scripting, data manipulation, automation), SQL (for data querying and extraction).

• Data Analysis & Modeling: Tools for statistical analysis, financial modeling, and potentially simulation.

Analytics & Reporting:

• Business Intelligence Tools: Experience with platforms like Tableau, Power BI, or Looker for creating dashboards and reports to communicate AI impact and portfolio performance.

• Spreadsheet Software: Advanced proficiency in Microsoft Excel or Google Sheets for financial modeling and data analysis.

CRM & Automation:

• CRM Systems: Familiarity with CRM platforms like Salesforce, HubSpot, or Microsoft Dynamics is beneficial, as many AI automation opportunities will relate to sales and customer success processes.

• Workflow Automation Tools: Understanding of broader automation concepts, even if not directly using specific RPA tools, is valuable.

• Integration Tools: Awareness of how AI solutions integrate with existing enterprise systems.

📝 Enhancement Note: Proficiency in Python and SQL is a baseline requirement. The emphasis on specific AI agent frameworks (LangGraph, AutoGen, MCP) indicates a need for candidates with practical experience in building more sophisticated AI applications beyond simple LLM prompting.

👥 Team Culture & Values

Operations Values:

• Innovation & Experimentation: A culture that encourages trying new approaches, learning from failures, and pushing the boundaries of what's possible with AI.

• Data-Driven Impact: Strong emphasis on using data to validate ideas, measure outcomes, and demonstrate tangible business value.

• Efficiency & Optimization: A commitment to identifying and implementing solutions that streamline processes and improve operational effectiveness.

• Collaboration & Transparency: Encouraging open communication, knowledge sharing, and teamwork across departments to achieve common goals.

• Agility & Adaptability: A willingness to embrace change, adapt to new technologies, and pivot priorities as needed in a dynamic field.

Collaboration Style:

• Proactive Partnership: Actively seeking out business needs and opportunities for AI-driven solutions.

• Bridging Gaps: Effectively communicating between technical and non-technical teams, ensuring alignment and understanding.

• Iterative Feedback Loops: Regularly engaging with stakeholders to gather feedback on prototypes and refine solutions.

• Knowledge Sharing: Contributing to a collective understanding of AI capabilities and best practices within the organization.

📝 Enhancement Note: The culture is geared towards innovation and rapid execution, which is crucial for roles involving AI prototyping. Operations professionals who thrive in environments that value experimentation and data-backed decision-making will be a good fit.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating Ambiguity: Dealing with unclear business requirements and rapidly evolving AI technologies requires strong critical thinking and adaptability.

• Rapid Prototyping & Validation: The pressure to quickly build and validate prototypes while maintaining quality and business relevance.

• Stakeholder Management: Effectively managing expectations of diverse stakeholders with varying levels of technical understanding.

• Keeping Pace with AI Advancements: Continuously learning and adapting to the fast-evolving landscape of AI tools, models, and methodologies.

• Translating Vision to Value: Demonstrating tangible ROI and business impact from experimental AI projects to secure further investment.

Learning & Development Opportunities:

• AI Specialization: Opportunity to become a subject matter expert in specific AI domains, agent frameworks, or LLM applications.

• Industry Exposure: Gaining insights into AI adoption across various regulated industries (healthcare, finance, insurance).

• Strategic Influence: Developing skills to advise executive leadership on AI strategy and digital transformation initiatives.

• Cross-functional Skill Building: Enhancing abilities in product thinking, project management, and stakeholder communication within an AI context.

• Formal Training & Certifications: Potential for company support for relevant AI/ML courses, certifications, or conferences.

📝 Enhancement Note: This role presents a significant opportunity for growth in the highly sought-after field of AI and automation. The challenges are framed as opportunities for skill development and strategic advancement.

💡 Interview Preparation

Strategy Questions:

• "How would you identify the top 3 AI automation opportunities within a typical sales development team?" (Focus on process: stakeholder interviews, data analysis, pain point identification).

• "Describe a situation where you had to build a business case for a new technology. What were the key metrics you focused on, and how did you present it to leadership?" (Focus on ROI modeling, stakeholder alignment, communication).

• "Given an ambiguous business problem, what steps would you take to translate it into a concrete AI use case with measurable success criteria?" (Focus on decomposition, requirement definition, validation planning). Company & Culture Questions:

• "How do you stay updated with the latest advancements in AI and LLMs?" (Demonstrates proactivity and passion for the field).

• "Describe your experience working in fast-paced, experimental environments. How do you handle shifting priorities?" (Assesses adaptability and comfort with ambiguity).

• "How would you ensure that your AI prototypes align with the overall business strategy and deliver measurable value?" (Focus on strategic alignment and impact measurement). Portfolio Presentation Strategy:

• Select High-Impact Examples: Choose 2-3 projects that best showcase your AI prototyping, business analysis, and ROI modeling skills.

• Storytelling: Frame each project as a narrative: the challenge, your innovative solution (prototype), the process, and the quantifiable results.

• Demonstrate Technical Depth: Be prepared to explain the AI tools and methods used and why you chose them.

• Quantify Everything: For each project, highlight the ROI, efficiency gains, or other key performance indicators achieved or projected.

• Practice Your Pitch: Rehearse your presentation to ensure it is concise, clear, and engaging, fitting within any time constraints.

📝 Enhancement Note: Interview preparation should focus on demonstrating a blend of analytical rigor, hands-on AI skills, and strategic business acumen. The ability to articulate the "why" and "how" of AI implementation, backed by data and clear communication, will be paramount.

📌 Application Steps

To apply for this operations position:

• Submit your application through the Jobgether platform via the provided link.

• Tailor Your Resume: Highlight experience in business analysis, AI/ML, LLMs, Python, SQL, ROI modeling, and any AI agent frameworks. Use keywords from the job description.

• Curate Your Portfolio: Prepare a digital portfolio (e.g., PDF, personal website, GitHub repository) showcasing relevant AI prototypes, business cases, and workflow documentation. Be ready to present key pieces.

• Prepare for AI/Technical Questions: Brush up on LLM prompting techniques, Python scripting for data analysis, and SQL querying. Review common AI use cases and ROI calculation methods.

• Research the AI Landscape: Familiarize yourself with current trends in AI automation and agent-based systems, particularly those relevant to business operations and GTM functions.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

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