Career Paths
Pick the data role that fits how you want to work.
16 source-backed roadmaps across analytics, data science, AI engineering, data engineering, decision science, healthcare, finance, and governance.
All LDS career roadmaps
Every path follows the same role-first structure: what to learn, why it matters, what to build, and how to prove readiness.
Data Analyst
Extract insights, build dashboards, and turn business questions into SQL, Python, and visualization workflows.
Entry-level analytics demand varies by market; treat this as a BI, reporting, and business analytics gateway.
Data Scientist
Use statistics, machine learning, experimentation, and product judgment to turn messy data into decisions.
BLS projects strong US growth for data scientists; global demand depends on AI adoption and local hiring cycles.
Machine Learning Engineer
Build, deploy, optimize, and maintain machine learning systems that serve real users.
ML engineering maps to software, data science, and AI deployment demand rather than one clean occupation code.
Data Engineer
Design pipelines, warehouses, lakehouses, and streaming systems that make data reliable.
Data engineering sits between software, database architecture, warehousing, and cloud infrastructure demand.
AI Engineer
Ship LLM applications, RAG systems, agents, evals, and production AI features.
AI engineering demand is global, but exact role definitions and pay vary sharply by product maturity and region.
Analytics Engineer
Turn raw warehouse data into governed models, metrics, tests, and trusted analytics surfaces.
Analytics engineering is a fast-moving title; use data warehousing and BI sources as stable market anchors.
MLOps Engineer
Keep ML systems alive with CI/CD, model serving, monitoring, feature stores, and production operations.
MLOps is best treated as a production-systems role shaped by AI adoption, software demand, and model governance.
Quantitative Analyst
Use mathematics, programming, statistics, and market structure to price risk and research trading ideas.
Quant hiring is concentrated by market, firm type, credential bar, and role track: research, dev, risk, or trading.
Forward Deployed AI Engineer
Embed with customers, scope valuable AI workflows, build production systems, and prove adoption with evals.
OpenAI and Palantir job descriptions show a real hybrid role: engineering, customer discovery, system design, deployment, and adoption.
Business Intelligence Analyst
Build trusted reporting, dashboards, KPI systems, and business intelligence workflows.
O*NET explicitly defines BI analyst work around querying repositories, reports, dashboards, patterns, and BI systems.
Product and Growth Analyst
Analyze funnels, experiments, retention, pricing, and product behavior to improve user outcomes.
This role sits between analytics, market research, product management, and experimentation rather than one official occupation code.
Data Architect
Design data models, warehouses, governance, integration patterns, and architecture decisions for scale.
BLS separates database architects from DBAs and projects stronger growth for architecture work.
Operations Research Analyst
Use optimization, simulation, forecasting, and analytics to improve operational decisions.
BLS projects strong US growth for operations research analysts, a distinct path from quant finance.
Applied Scientist
Bridge research and product by designing experiments, models, papers, prototypes, and measurable AI improvements.
Applied scientist roles are concentrated in AI labs, search, recommendations, ads, robotics, and product ML teams.
Health Data Scientist
Analyze clinical, claims, public health, and biomedical data with statistical rigor and privacy-aware workflows.
O*NET includes biostatisticians, clinical data managers, health informatics specialists, and data scientists in its Data Science and AI cluster.
Data Governance and AI Risk Analyst
Make data and AI systems trustworthy through governance, quality, lineage, controls, evaluation, and risk documentation.
AI adoption is increasing demand for judgment, governance, compliance, data quality, and risk skills around automated systems.