Data & Analytics
Turning raw data into decisions and ensuring data quality for AI systems. Covers data analysis, data science (business analytics context), data engineering (analytics platform context), analytics engineering, BI, data governance, data platform engineering, applied ML for business use cases, and ML data annotation operations management.
Roles
The canonical roles within Data & Analytics.
Data Scientist
Data Scientists in these roles build predictive and classification models that directly drive business outcomes, from revenue optimization and customer health scoring to autonomous vehicle performance evaluation and capacity planning. They distinguish themselves by owning problems end-to-end—from translating ambiguous stakeholder questions into measurable problems, through model development and validation, to production deployment and ongoing monitoring. These roles typically sit within cross-functional product, operations, or analytics teams at scale-up and enterprise AI companies, partnering closely with engineering, product, and business leaders to ensure models deliver sustained impact and reliability in real-world systems.
Data Engineer
This role involves building and optimizing the data infrastructure that powers analytics, machine learning, and operational decision-making across AI-focused organizations. Data engineers in this position design scalable pipelines to ingest data from infrastructure, product systems, and business operations, then transform that raw data into reliable datasets that serve analysts, data scientists, and product teams. What sets this role apart is its foundation-level focus—rather than analyzing data or building models, these engineers architect the systems, data models, and warehouses that make all downstream work possible. They typically report into data or platform leadership and work cross-functionally with product, engineering, finance, and operations teams to translate business requirements into production-grade data infrastructure that scales with organizational growth.
ML Data & Annotation Operations
This role leads the end-to-end data operations lifecycle for machine learning systems, translating research and product requirements into scaled annotation workflows and quality standards. Professionals in this position design data collection strategies, manage vendor partnerships and internal labeling teams, and establish comprehensive quality frameworks including guidelines, metrics, and escalation processes. Unlike individual contributors focused solely on annotation tasks, these operators own strategic decisions around tooling, process optimization, and workforce development to ensure datasets meet rigorous quality standards at scale. They typically report to heads of data or research operations and collaborate directly with ML engineers, researchers, and product teams to align data needs with model training priorities.
Data & Business Analyst
Data analysts in this role work within cross-functional AI teams to translate complex operational and product data into strategic insights that drive autonomous vehicles, cloud infrastructure, or revenue intelligence platforms forward. They distinguish themselves through deep technical execution—building scalable data pipelines and advanced SQL models that surface not just what happened, but why it matters for the business—while partnering closely with product, engineering, and leadership to shape high-stakes decisions. These analysts typically sit within dedicated analytics or data science teams embedded in larger organizations, serving as bridges between technical data infrastructure and business strategy in fast-moving AI companies.
Analytics Engineer
Analytics Engineers at AI companies sit between data engineering and analytics, building and maintaining the data models, metrics layers, and self-serve analytics that the rest of the company relies on to make decisions. The day-to-day is SQL- and dbt-heavy: designing dimensional schemas and warehouse models, defining metric logic that holds across teams, building documentation and tests, and partnering with finance, product, and GTM stakeholders on what the numbers should mean. Where the role differs from data engineering is in proximity to business questions—Analytics Engineers spend more time defining metrics and enabling self-service than building ingestion pipelines, even when the technical surface looks similar. Specific data domains range from product usage and revenue (most companies) to compute and infrastructure economics (at AI infrastructure companies), but the underlying methodology is the same.
Marketing & GTM Analytics
This role serves as the strategic and operational backbone of AI company go-to-market teams, designing measurement frameworks that connect marketing spend to pipeline and revenue outcomes. Practitioners build attribution models, manage complex marketing technology stacks, and translate funnel data into executive narratives that drive budget allocation and campaign optimization decisions. They distinguish themselves by combining deep analytical rigor—whether through multi-touch attribution, incrementality testing, or marketing mix modeling—with hands-on infrastructure work, often owning data pipelines, dashboards, and automation across tools like Marketo, Salesforce, and modern data warehouses. These roles typically sit within dedicated Marketing Operations or GTM Analytics teams that partner closely with both marketing leadership and cross-functional stakeholders in sales, product, and finance, serving as the trusted data authority that enables the entire revenue organization to operate on clean, well-defined metrics.
Data & Analytics Leader
This leader owns the strategic vision and operational execution of data teams that unlock insights driving business outcomes across AI-driven products. They architect scalable data infrastructure and governance frameworks while partnering with cross-functional executives to translate complex data into actionable intelligence that shapes product decisions, operational efficiency, and market strategy. The role distinguishes itself by requiring both hands-on technical depth and organizational leadership—these leaders remain immersed in analytics and data engineering work while building high-performing teams and setting standards for analytical rigor. They typically report to C-suite executives in growth-stage or scale-up AI companies, operating at the intersection of product, engineering, and business strategy where data becomes the foundation for competitive advantage.
Recent Jobs
The latest Data & Analytics openings across the AI industry.