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BearPlex machine learning consultants combine deep ML expertise with executive-grade communication: leading ML strategy, model architecture decisions, vendor / framework evaluation, ML governance. Senior advisors for organizations where ML is strategic but the path forward is unclear.
What a Machine Learning Consultant actually does at BearPlex
A machine learning consultant at BearPlex brings senior ML expertise to organizational and architectural decisions. The role spans: ML strategy and roadmap (where to invest, build vs buy, how to sequence), ML architecture review (audit existing systems, identify risks and opportunities), ML vendor evaluation (frameworks, platforms, tooling), model risk management for regulated industries, ML governance framework design, technical due diligence on ML products (for investors and acquirers), and the executive communication that translates ML decisions into business outcomes. Our consultants are senior ML practitioners (typically 10+ years) who've shipped production ML themselves and bring those scars to advisory work. Standard engagements include ML Discovery Sprints (1-2 weeks of intensive scoping), ML Architecture Reviews (3-4 weeks of audit + recommendations), Strategy Engagements (4-8 weeks producing roadmap + governance + execution plan), and Fractional Head of ML arrangements.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Led ML strategy for a top-15 US bank's transformation: designed governance framework that supported 40+ ML initiatives in production with examiner-defensible model risk management.
Advised a healthcare AI startup on ML strategy and FDA SaMD path: strategy directly contributed to regulatory clearance + Series B raise.
Designed ML platform strategy for a Fortune 500 retailer: replaced fragmented per-team ML infrastructure with shared platform that supports 60+ ML use cases.
Senior advisor to multiple Series A-B AI startups on ML architecture decisions: track record of advice that aged well over 3+ years.
Skills matrix
The capabilities every BearPlex Machine Learning Consultant brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| ML strategy and roadmap development | Expert | Strategy frameworks · Discovery Sprint methodology |
| ML architecture review and audit | Expert | Code review at scale · Architecture review · Risk assessment |
| Model risk management (OCC 2011-12, SR 11-7) | Expert | MRM documentation · Validation frameworks · Examiner-readiness |
| ML vendor and framework evaluation | Expert | Benchmark design · TCO modeling · Framework comparison |
| ML governance framework design | Expert | NIST AI RMF · ISO 42001 · Sector-specific frameworks |
| Technical due diligence (ML systems) | Expert | ML system audit · Architecture review · Risk assessment |
| MLOps maturity assessment and roadmap | Expert | Maturity frameworks · Capability assessment · Roadmap design |
| Build vs buy frameworks for ML systems | Expert | TCO modeling · Strategic analysis · Vendor evaluation |
| Sector-specific ML expertise (FS, healthcare, retail) | Expert | Sector regulation knowledge · Domain-specific architecture |
| Executive communication and board presentation | Expert | Strategic narratives · Investment cases |
| Fractional Head of ML / advisory | Advanced | Senior leadership advisory · Team coaching · Hiring support |
| ML cost modeling and unit economics | Expert | Inference cost modeling · Per-model economics |
How we vet machine learning consultants
Senior interview
60-minute deep-dive on past advisory work. We probe: did the consultant produce decisions that aged well? Did they push back on bad ideas with rigor? Can they translate ML depth into executive communication? We screen out advisors whose 'consulting' was selling a vendor's product.
Live case exercise
We give the candidate a realistic strategic problem (build vs buy ML platform, vendor selection, ML organization design, M&A ML due diligence) with materials and 90 minutes. They must produce a written analysis with clear recommendations.
Reference deep-dive
Three reference checks with previous clients: focused on whether the consultant's recommendations actually played out as predicted over 12+ months.
Hamad-led trial engagement
Trial engagement on a real client problem, supervised directly by Hamad Pervaiz. ML consulting requires senior judgment that's hard to test in interview; the trial proves it.
What clients say
“Their ML consultant ran a 6-week strategy engagement that fundamentally changed our ML roadmap. Specifically: he killed two projects we were about to staff and identified a third that became our biggest ML success the following year.”
“Best ML technical due diligence I've worked with. Their consultant identified a fundamental flaw in our acquisition target's MLOps that other diligence missed. We adjusted deal terms.”
“Their ML consultant brought senior depth to our board ML committee: neither pure technical nor pure strategy, the rare combination that drives good decisions.”
Hiring machine learning consultants: questions answered
Yes: common engagement type. We've performed ML technical due diligence on 20+ companies for venture capital, private equity, and strategic acquirers. Standard scope: model audit, MLOps assessment, ML team and capability evaluation, IP and data licensing analysis, recommendations on deal terms.
Yes: for select engagements. We have a small bench of senior consultants who do these arrangements (typically 0.4-0.6 FTE) for AI-heavy product teams that need senior ML leadership without full-time hire. Hamad Pervaiz personally takes a small number of these engagements.
Yes: common engagement scope. We've designed and audited MRM frameworks for OCC 2011-12 / SR 11-7 (banking), Federal Reserve supervisory guidance, sector-specific frameworks (FDA SaMD for healthcare, NAIC ORSA for insurance). Our MRM work has passed first-line and second-line review at major US banks.
ML Discovery Sprint: $25K-$50K (1-2 weeks). ML Architecture Review: $40K-$120K (3-4 weeks). ML Strategy engagement: $80K-$250K (4-8 weeks). Fractional Head of ML: $25K-$45K monthly retainer.
Hamad Pervaiz (Founder & CEO) is based in Lahore, Pakistan with regular travel to Tokyo and the US. Our broader consultant team is globally distributed. For US-based engagements requiring more synchronous work, we have consultants in PST / EST time zones.
Generalist depth across classical ML, deep learning, MLOps, modern LLM systems. Sector-specific expertise in financial services (banking, asset management), healthcare (clinical ML, FDA SaMD), retail / ecommerce (recommendations, personalization), B2B SaaS (multi-tenant ML). For other sectors we staff with consultants who have direct sector experience.
We're engineers, not deck-makers. Our consultants have shipped production ML themselves and bring those scars to advisory work. We optimize for outcomes that age well, not for engagement length. For ML-heavy strategic decisions, this depth matters more than the brand and breadth that traditional consultancies offer.
Featured case studies
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