The Eval Advisory Career Path: From Practitioner to Strategic Advisor

Stage Role Focus Typical Experience Primary Output Compensation Practitioner Execute evaluation, run metrics, build benchmarks 1-3 years eval work Evaluation results, datasets, models $100K-180K salary Senior Practitioner Design eval programs, mentor others, establish eval standards 3-6 years eval work Eval frameworks, team scaling, process documentation $150K-220K salary Internal Advisor Strategy for org's eval program, cross-team consulting 6+ years, Chief Eval Officer or Lead role Strategic recommendations, org alignment $200K-350K salary/equity External Consultant Project-based advisory to multiple organizations Senior practitioner + 1-3 years advisory Strategic plans, audits, implementations $250-500/hour or $50K-500K+ per project Thought Leader Shape the field, publish research, speak, influence standards 5-10+ years, multiple advisory engagements Publications, frameworks, conference talks, best practices Speaking fees, board positions, thought leadership income

Core Competencies for Eval Advisors

Technical Depth

You must have deep, genuine expertise in evaluation. This means you've built evals that failed, shipped evals to production, dealt with real data quality issues, and learned from mistakes. No amount of consulting skill compensates for shallow technical knowledge. Clients can smell it.

Stakeholder Communication

Advisory is ultimately about influence. You need to translate between executives (who care about business outcomes) and practitioners (who care about technical rigor). You must present the same finding to a CEO, a VP of Engineering, and a data scientist—each in language that resonates with their priorities.

Business Acumen

Understanding the business context is non-negotiable. Why is the organization investing in eval? What problem does it solve? What's the ROI? An eval advisor without business sense gives technically perfect recommendations that organizations ignore because they don't align with business reality.

Regulatory and Compliance Knowledge

Modern eval advising touches on regulation: EU AI Act, FDA guidance for AI in healthcare, FTC scrutiny of algorithmic bias, etc. You don't need to be a lawyer, but you must understand how regulatory environments shape eval requirements.

Organizational Psychology

Many eval initiatives fail not because the methodology is wrong, but because the organization isn't ready, incentives are misaligned, or there's political resistance. Strong advisors understand organizational culture, power dynamics, and change management. You learn to ask: "Who benefits from better eval? Who loses?" and navigate those tensions.

Judgment and Pattern Recognition

After years of advisory work, you develop pattern recognition: "This organization will struggle with X" or "This team is ready for Y but not Z." This intuition is built on accumulated experience. Early-stage advisors don't have it yet; that's okay. It develops with deliberate reflection.

Building Your Advisory Practice: Independent vs. Embedded

Independent Advisory Practice

Model: You're a solo consultant or small firm. Clients engage you for specific projects (maturity assessment, program design, implementation support).

Pros:

• High leverage. Your time is valuable; you bill accordingly.
• Portfolio diversity. Work with 5-10 different organizations, learning patterns across contexts.
• Autonomy. You choose clients and projects.
• Thought leadership. Published insights from your advisory work build credibility.

Cons:

• Business development burden. You must constantly acquire new clients.
• Income volatility. Projects are lumpy; you may have feast-famine cycles.
• Solo risk. Illness, burnout, or market downturns hit you directly.
• Operations burden. Contracts, invoicing, taxes, insurance—you handle it all.

Typical revenue: $200-500/hour or $50K-500K per project. 3-4 major engagements per year = $200K-$1M+.

Embedded in Consulting Firm

Model: You work for a consulting firm (Big 4, boutique AI consultancy, product company). You advise clients as part of a larger service offering.

Pros:

• Steady income. Salary + bonus + benefits.
• Business development support. The firm brings leads; you focus on delivery.
• Scaling. Work with a team of practitioners; leverage their time.
• Prestige. Affiliation with reputable firm builds credibility.

Cons:

• Lower individual leverage. You split billable hours with the firm and other team members.
• Less autonomy. Client selection, pricing, scope—driven by firm strategy.
• Broader demands. You may be asked to do sales, proposal writing, team management.
• Narrow specialization pressure. Firm may pigeonhole you into a specific vertical.

Typical compensation: $150K-250K base salary + 20-40% bonus, depending on seniority and billable utilization.

Types of Advisory Engagements

Eval Maturity Assessment

Duration: 2-4 weeks. Engagement size: $10K-50K.

Scope: You audit the organization's current eval practices—what they're doing well, where they're weak, what's missing. Interview 5-10 key stakeholders (practitioners, managers, execs). Review existing evals. Assess against a maturity framework (eval.qa or similar). Deliver a report with findings and roadmap.

Client value: Clarity on current state, external validation of concerns, prioritized list of improvements.

Eval Program Design

Duration: 6-12 weeks. Engagement size: $50K-200K.

Scope: You design a comprehensive evaluation program aligned to the organization's strategy. Work with stakeholders to define evaluation principles, prioritized eval questions, methodology, tooling, team structure, and success metrics. Deliver a detailed program plan and implementation roadmap.

Client value: Strategic alignment, clear prioritization, reduced implementation risk.

Expert Witness / Third-Party Audit

Duration: 2-6 weeks per case. Engagement size: $20K-100K+.

Scope: You evaluate an AI system's performance claims for legal/regulatory purposes. Examine documentation, reproduce evaluations if possible, assess methodology rigor, and provide expert opinion on whether claims are supported. Common in product liability, discrimination claims, or regulatory disputes.

Client value: Credible expert testimony, methodology validation.

Board Advisory

Duration: Ongoing retainer. Engagement size: $10K-30K/month.

Scope: You serve as informal advisor to a startup or company's board, advising on AI quality strategy, regulatory risk, and eval-related questions. May be formal (board position with meetings) or informal (ad-hoc consulting).

Client value: Strategic guidance, board-level credibility, risk mitigation.

Typical Advisory Engagement Structure

1. Discovery (1-2 weeks): You meet with key stakeholders. Understand the problem, constraints, politics, timeline, budget. Define success criteria. This is where you assess whether you can actually help.
2. Assessment (2-4 weeks): Deep dive into current state. Review existing evals, talk to practitioners, understand data/tools. Identify gaps.
3. Recommendations (1-2 weeks): Synthesize findings into a prioritized set of recommendations. Typically: 3-5 major initiatives, each with rationale, implementation steps, timeline, and estimated effort.
4. Implementation Support (ongoing): Help the organization execute recommendations. This may be limited (hand off after recommendations) or extended (embedded support for 3-6 months while they build out the program).
5. Monitoring (quarterly): Check in periodically to assess progress, adjust recommendations based on learnings, and provide ongoing guidance.

Deliverables typically include: Discovery summary, assessment report, prioritized recommendations document, implementation roadmap, and optionally: templates, sample eval designs, tooling recommendations.

Pricing an Eval Advisory Practice

Hourly Billing

Rate range: $250-500/hour for independent advisors, depending on experience and specialization. Specialists (AI safety, healthcare compliance) command higher rates.

Pros: Simple, transparent, aligns incentives (more work = more pay).

Cons: Discourages efficiency (if you solve the problem in 20 hours instead of 40, you lose revenue). Clients may pressure you to extend engagements.

Typical engagement: 100-150 billable hours at $300/hour = $30K-45K.

Project-Based Pricing

Model: Fixed fee for a defined scope of work. Example: "Eval maturity assessment and roadmap = $40K."

Pros: Aligns incentives toward efficiency and value. Clients prefer fixed fees (budgeting certainty). Encourages you to work smart, not just long.

Cons: Requires excellent scoping or you'll underprice. Scope creep can destroy profitability.

Typical pricing: $20K (small assessment) to $200K+ (full program design and implementation support).

Retainer Models

Model: Monthly recurring fee ($5K-30K/month) for ongoing advisory access, strategy updates, implementation support.

Pros: Predictable revenue. Deep client relationships. You understand their business deeply over time.

Cons: Risk of becoming order-taker instead of strategic advisor. Difficult to scale (retainers lock up your time).

Typical model: Startup or mid-market company retains you 3-6 months post-engagement to ensure successful implementation, then may extend if value is clear.

Building Credibility and a Client Base

Thought Leadership

Write and publish. Start with blogs (3-4 per month), graduate to longer articles on platforms like Towards Data Science or your own Substack. Eventually, aim for peer-reviewed publications in academic venues (NeurIPS, ACL, ICLR eval tracks). Thought leadership establishes authority and attracts inbound opportunities.

Speaking and Conferences

Present at conferences (NeurIPS, ICML, ACL, specialized AI safety conferences). Submit to CFPs aggressively. Good talks attract clients and show you can articulate ideas clearly—a key advisor skill.

Certifications and Credentials

Pursue Level 5 eval.qa certification and other relevant credentials (if in healthcare, HIPAA expertise; if in regulated spaces, compliance background). Credentials signal credibility to organizations unfamiliar with you personally.

Case Studies and References

Document your advisory work (with client permission and anonymization if needed). Create 2-3 detailed case studies showing the problem, your approach, and outcomes. References from past clients are gold.

Network Building

Advisory is ultimately a relationship business. Attend industry events, maintain connections with practitioners and execs you've worked with, join relevant communities (AI safety, applied ML, etc.). Your next client often comes through a referral.

Strategic Partnerships

Partner with related advisors. If you specialize in eval design but clients also need tooling recommendations, partner with product specialists. These partnerships extend your reach and make you more valuable to clients.

Advisory Pitfalls and How to Avoid Them

Scope Creep

The trap: Client asks for just a bit more analysis, one more recommendation, another interview. Before you know it, you've delivered 200 hours of work for a 100-hour contract.

Prevention: Define scope explicitly in writing. Create a change request process. Track time. When scope expands, discuss timing and budget impact with the client.

Conflict of Interest

The trap: You advise an organization to adopt tooling from a vendor you have financial interest in. Or you recommend approaches that maximize your own follow-on work.

Prevention: Disclose conflicts transparently. Recommend based on organizational benefit, not your benefit. If there's a potential conflict, let the client know upfront.

Staying Current

The trap: The field evolves rapidly. Your 2024 framework becomes outdated by 2026. Clients expect you to be current.

Prevention: Dedicate 10-20% of your time to staying current. Read papers, experiment with new methods, talk to practitioners, attend conferences. This is investment in your credibility.

Over-Customization

The trap: Every client is unique, so you design completely custom recommendations for each. This doesn't scale and wastes time.

Prevention: Develop reusable frameworks and templates. Customize the application, not the core approach. This allows you to be more efficient while still tailoring to each client.

Implementation Risk

The trap: You deliver a great strategic recommendation, but the client fails to execute. This damages your reputation and referral potential.

Prevention: Include implementation support in your engagement. Don't just hand off a report; help them execute. This increases engagement value and ensures recommendations actually happen.

Case Study: Three Advisory Engagement Types

Startup AI Safety Audit ($25K, 3 weeks)

Client: AI safety startup building an automated system for detecting biological hazard risks in research papers. Seeking to raise Series A and needed credible eval methodology.

Engagement:

• Week 1: Understand the system and intended use case. Review existing eval attempts. Interview founders and safety team.
• Week 2: Propose robust eval design: what does success look like? Design evaluation against false positive rates (blocking legitimate research), false negative rates (missing hazards), and coverage (across hazard types).
• Week 3: Deliver report with methodology, sample evaluation on 100 papers, and recommendations for series A review process.

Outcome: Founders were able to show investors a credible eval framework. The report became part of Series A diligence materials. Advisor stayed on as part-time advisor post-investment to support eval maturation.

Enterprise Eval Program Design ($150K, 12 weeks)

Client: Large financial services company deploying ML models for lending decisions. Regulatory pressure (FCRA, fair lending) required comprehensive eval program.

Engagement:

• Phase 1 (2 weeks): Stakeholder interviews with compliance, model development, and business teams. Understand regulatory requirements and organizational constraints.
• Phase 2 (3 weeks): Assessment of current eval practices. Audit existing models' documentation.
• Phase 3 (4 weeks): Design comprehensive eval program: fairness metrics, performance monitoring, governance structure, tooling recommendations, team sizing.
• Phase 4 (3 weeks): Implementation support. Help build the evaluation platform, train team, establish processes.

Outcome: Organization deployed a mature eval program that integrated with existing ML governance. Demonstrated compliance to regulators. Advisor transitioned to 6-month retainer supporting implementation.

Regulatory Compliance Review ($75K, 4 weeks)

Client: Pharmaceutical company under FDA scrutiny for an AI diagnostic system. FDA alleged insufficient eval for different demographic groups.

Engagement:

• Week 1: Review all eval documentation, training data, test data, performance reports.
• Week 2: Conduct independent evaluation on held-out test set, specifically analyzing performance by demographic groups.
• Week 3: Prepare expert report assessing whether company's claims are supported and identifying gaps.
• Week 4: Present findings to company leadership and prepare for FDA response.

Outcome: Expert report identified genuine weaknesses in demographic coverage. Recommendations led to retraining on diverse data and expanded clinical trial. Company was able to demonstrate good-faith effort to FDA, avoided penalties.