Deep expertise across the disciplines and industries that run on data.
From data science and analytics to data engineering, machine learning, technology, product management, and digital marketing — Burtch Works has built specialized candidate networks in every discipline and every major industry vertical for 15+ years.
Every industry runs on data. The disciplines that power it vary by challenge.
Burtch Works has spent 15+ years building not just industry-specific candidate networks, but discipline-specific depth within each industry. A healthcare organization's data engineering needs look fundamentally different from their population health analytics needs — and both look different from a financial services firm's quantitative modeling requirements or a consumer goods company's digital marketing analytics function.
We place talent across core disciplines, embedded AI Transformation: Data Science & Analytics, Data Engineering, Machine Learning & AI, Technology, Product Management, and Digital Marketing. Within each discipline, our candidate networks are built specifically for the industry contexts where that work happens — so we understand not just what the role requires technically, but what it requires to be effective in your specific operating environment.
For private equity firms managing complex portfolios, this means consistent talent frameworks across every portfolio company's discipline mix. For enterprise organizations navigating AI transformation, it means delivery models that address workforce gaps across the full data and technology stack — not just the analytics function in isolation.
We know the difference. And we know the candidates.
INDUSTRIES WE SERVE
For private equity firms managing complex portfolios, this means consistent talent frameworks across every portfolio company's discipline mix. For enterprise organizations navigating AI transformation, it means delivery models that address workforce gaps across the full data and technology stack — not just the function in isolation.
Data science, analytics, data engineering, and ML talent deployed across PE portfolio companies — from due diligence analytics assessments to post-close capability buildouts, analytics operating model design, and AI readiness initiatives at every stage of the investment lifecycle.
Common Challenges:
Scaling analytics and AI capabilities across portfolio companies quickly and consistently.
Leadership gaps in data science and AI at the most value-sensitive stages of the investment lifecycle.
AI and data readiness assessments ahead of acquisition or exit.
Speed-to-execution pressure that traditional hiring timelines can't meet.
Demand forecasting, customer analytics, pricing intelligence, supply chain optimization, digital marketing analytics, and ML engineering for consumer goods companies and retail organizations navigating omnichannel complexity and AI-augmented consumer intelligence.
Common Challenges:
Demand sensing and supply chain analytics in volatile consumer environments.
Customer personalization at scale requires real-time ML capabilities.
AI-enabled consumer insights and pricing intelligence.
Omnichannel analytics talent gaps across digital and physical retail
chain optimization.
Content recommendation, ML engineering, audience intelligence data science, advertising measurement analytics, streaming platform data engineering, and digital marketing analytics for entertainment and media organizations managing platform transition and AI disruption simultaneously.
Common Challenges:
Generative AI disruption to content creation, rights management, and audience analytics.
Data-driven audience strategy across fragmented streaming and digital platforms.
Content optimization and recommendation algorithm talent.
AI-enabled workflows and scalable digital operations.
Quantitative analytics, risk modeling, fraud detection, model risk governance, data engineering, ML engineering, and AI governance for banks, insurers, asset managers, and fintech organizations operating under regulatory constraints and competitive technology pressure.
Common Challenges:
Model risk governance and AI compliance under evolving regulatory frameworks.
Quantitative talent scarcity across risk modeling, fraud detection, and algorithmic analytics.
AI governance and responsible AI deployment under SEC and prudential regulatory pressure.
Data modernization and cloud migration in highly regulated data environments.
Solutions
Population health data science, clinical AI, revenue cycle analytics, health equity research, healthcare data engineering, and clinical informatics for health systems, payers, pharmaceutical companies, and health technology organizations, where regulatory compliance and clinical context shape every talent decision.
Common Challenges:
Clinical AI deployment under FDA Software as Medical Device frameworks.
Population health data science at scale across attributed patient populations.
Data interoperability and EHR analytics in fragmented health data environments.
AI readiness in revenue cycle, health equity, and operational analytics functions.
Predictive maintenance data science, supply chain analytics, operations research, IoT data engineering, industrial ML engineering, and technology infrastructure for manufacturing, logistics, energy, and industrial operations organizations, turning operational data into measurable performance improvement.
Common Challenges:
- Operational efficiency
- Infrastructure modernization
- Manufacturing intelligence
- Workforce scalability
- Predictive quality and yield optimization analytics requiring domain-specific ML expertise.
Litigation analytics, contract intelligence, legal NLP engineering, legal operations data science, discovery AI, and technology implementation for law firms, corporate legal departments, and legal technology companies building the analytics capabilities the profession now demands.
Common Challenges:
Generative AI deployment for contract analysis, discovery automation, and legal research augmentation.
Knowledge management and institutional knowledge capture at scale.
Workflow modernization and legal operations analytics.
Data governance under attorney-client privilege and confidentiality constraints.
AI-assisted legal operations and billing analytics.
Specialized legal NLP talent that generalist firms can't access.
Product analytics, growth data science, ML engineering, MLOps, AI platform architecture, data engineering, software engineering, and product management for software, SaaS, platform, and enterprise technology organizations where analytics and engineering capability define competitive position.
Common Challenges:
Product analytics and ML engineering talent in a market where platform companies compete for the same candidates.
Infrastructure scaling and AI platform architecture require specialized depth.
Foundation model deployment and LLM engineering — a category that didn't exist as a hiring requirement two years ago.
AI and cloud modernization require practitioners who bridge legacy systems and modern ML infrastructure.
Burtch Works combines industry context, specialized expertise, workforce intelligence, and flexible delivery models to help organizations navigate transformation with greater clarity, speed, and operational alignment.
HOW WE ENGAGE
Industry depth that changes what's possible in the search.
We don't assess candidates against a job description. We assess them against the specific industry context they'll be operating in — clinical workflow knowledge in healthcare, regulatory fluency in financial services, and operational domain depth in industrials. Technical capability is the baseline. Industry fluency is the differentiator. The assessment criteria change because the work changes.
Our compensation benchmarks are drawn from 15+ years of actual placement activity across every industry vertical we serve — not public salary surveys that lag the market by 12 to 18 months. When we tell you what a role should pay in your industry, it's because we placed that role last quarter.
Across all disciplines and eight industry verticals, we're activating established relationships — not starting searches from scratch. The data engineer with clinical data environment experience, the ML engineer with financial regulatory awareness, the digital marketing analyst with consumer domain fluency — these candidates are already known to us. So, each search starts closer to the finish line.
The right engagement model for a Private Equity portfolio company mid-integration is different from the right model for an enterprise healthcare system standing up a population health function. We match the delivery approach — retained search, fractional leadership, staff augmentation, or deliverable-based outcomes — to the industry context, not just the role.
RESULTS OUR CLIENTS MEASURE
What success looks like across our three engagement models.
3× Faster
Transformation roadmaps executed in weeks, not quarters. Operating models built for scale. AI capabilities that work — not just documented.
30-Day Fill Rate
Reduced hiring time and delivery risk without compromising the quality and specialized expertise of talent teams' needs.
94%
Client retention rate — a reflection of the trust built through consistent delivery, honest communication, and measurable outcomes.
100K+
A curated talent database of 100,000+ interview-ready professionals — highly qualified, technically assessed, and aligned to salary expectations.