Analytics and AI talent for tech firms where data drives products, revenue, and competitive advantage.
Product analytics, growth data science, ML engineering, MLOps, AI platform architecture, data engineering, product management, and digital marketing analytics — Burtch Works connects technology companies with the specialized talent that powers differentiated products and the strategies needed for sustainable growth across every data and technology discipline.
In technology, the AI and Data analytics talent gap is a product gap.
McKinsey's 2025 State of AI research shows that 88% of organizations now use AI in at least one business function — up from 78% the year prior. But usage and capability are different things, and the technology organizations pulling ahead aren't just using AI tools. They're deploying the right combination of data science, engineering, ML, and product talent to embed intelligence into core products, optimize growth loops, and build the proprietary data capabilities that compound competitive advantage over time.
That means product analytics leaders who can translate behavioral data into product decisions that improve user outcomes, ML engineers who can fine-tune foundation models for domain-specific applications and manage inference infrastructure at scale, data engineers who can build the data platform infrastructure that ML systems require to perform reliably in production, software engineers who can integrate AI capabilities into product architectures without creating technical debt, and product managers who understand ML system capabilities and limitations deeply enough to make sound product decisions.
It also means digital marketing analytics professionals who connect data science output to growth performance — acquisition, activation, retention, and monetization analytics that turn analytical capability into measurable revenue. Half of all global venture capital funding in 2025 went to AI start-ups. The competition for the practitioners who can build these systems — across every discipline — is fiercer than at any point in the industry's history.
Burtch Works has built candidate networks across all six technology disciplines for 15+ years, with relationships built on trust rather than cold outreach.
RECOMMENDED SOLUTIONS - Technology
Tailored for the technology company operating environment.
Whether you need a senior data or analytics leader deployed within the week, a full managed delivery team, or a strategic advisory engagement — we have the right path for your timeline and structure.
Product Analytics, ML & Data Engineering Talent
Specialized placement of product analytics managers, growth data scientists, ML engineers, MLOps engineers, data engineers, and AI platform architects from our deep technology sector candidate network.
Retained Executive Search
Dedicated search for VP Data Science, Head of ML, Director of Product Analytics, Head of Data Engineering, and senior AI platform leadership in SaaS, enterprise tech, and high-growth technology organizations.
AI Workforce Modernization
Redesign the analytics, data engineering, and AI org structure your technology company needs as the boundaries among ML engineering, AI engineering, and data science continue to evolve.
Staff Augmentation
Deploy specialized ML engineers, data engineers, product analysts, and digital marketing analytics practitioners for critical sprints, launches, or scaling phases without the overhead of permanent hires.
RECRUITER & TALENT INTELLIGENCE - TECHNOLOGY
Current trends in technology organizations highlight the demand for ML engineering, data engineering, product analytics, and AI platform talent.
62%
Average global wage premium for workers with AI skills, reflecting sustained competition for machine learning, data engineering, and AI product talent.
PwC Global AI Jobs Barometer 2026
88%
Organizations now use AI in at least one business function, up from 78% the previous year.
McKinsey State of AI 2025
86%
Technology employers expect AI and information-processing technologies to transform their businesses by 2030.
World Economic Forum Future of Jobs Report 2025
33.5%
Projected employment growth for Data Scientists from 2024 to 2034, creating approximately 23,400 openings annually.
U.S. Bureau of Labor Statistics Employment Projections 2025
Market signals -Technology
The foundation model era is reshaping needs in ML engineering, data engineering, and product management, yet job descriptions lag behind.
The technology talent market now demands professionals who can integrate foundation models, data infrastructure, and AI product strategy. Roles such as machine learning engineers skilled in inference optimization, data engineers familiar with vector databases, and product leaders who can apply large language models to enterprise products are the most challenging to fill.
Stanford HAI's AI Index 2026 found AI skills appearing in approximately 2.5% of all U.S. job postings—up roughly 55% year over year—underscoring how rapidly AI capabilities are becoming a baseline expectation across the technology workforce.
Growth-stage technology companies are competing for AI talent against platform employers long before candidates ever enter the market.
Top machine learning engineers, data engineers, AI product leaders, and analytics professionals are rarely active job seekers. Burtch Works recruiters often engage these candidates through trusted relationships before they explore new opportunities. Companies that don't structure roles, compensation, or value propositions effectively may find themselves at a disadvantage from the start.
Product analytics is evolving into traditional experimentation and AI-native intelligence, yet many organizations are hiring incorrectly for both data science and product management roles.
The former focuses on funnel analytics, while the latter requires building feedback loops and behavioral analytics for AI features. Most job descriptions target the first but miss the second.
AI talent has changed the economics of technology hiring.
PwC's 2026 Global AI Jobs Barometer reports a 62% wage premium for AI-skilled workers, highlighting the increasing value of hybrid technical expertise. Organizations using outdated compensation models struggle to attract experienced AI professionals.