Data Scientist
Builds statistical and machine learning models to uncover patterns in data and predict future outcomes.
Takes machine learning models from prototype to reliable, scalable production systems.
Also known as: ML Ops Engineer, Applied ML Engineer
Compare with another careerImplements and optimizes machine learning models designed by data scientists or researchers, and builds the training and serving infrastructure around them. Monitors model performance in production, writes production-quality code, and works with standard software engineering practices such as version control, testing, and deployment pipelines.
Desk-based software engineering environment, typically within a machine learning or AI platform team, working closely with data scientists and backend engineers.
Typical entry point
Junior ML engineer role in a tech company or AI-focused startup, often after some data science or software engineering experience
Core
Core
Also relevant
These are useful, relevant skills to build over time — not a checklist you need to already have.
These are common routes in, not the only path — and not a university or course recommendation.
General tendencies for this career, not a guarantee — actual roles vary by employer and location.
Builds statistical and machine learning models to uncover patterns in data and predict future outcomes.
Builds applications and systems powered by AI models, integrating them into products people actually use.
Designs and maintains the pipelines and infrastructure that move and store data reliably at scale.
Builds mathematical models used to price financial instruments, manage risk, and inform trading strategies.
Builds and maintains dashboards and reporting systems that give business leaders an ongoing view of performance.
Turns raw business data into clear reports and dashboards that help teams make decisions.