Answers business questions with data and statistics. Closer to analysis and decisions than to shipping software.
A data scientist answers business questions with data. The role sits between engineering and business — you need enough technical skill to build models and write code, and enough domain knowledge to ask the right questions and communicate findings to non-technical stakeholders.
Data science is broader and more established than the newer AI engineering roles. It includes traditional statistics, machine learning, experiment design (A/B testing), forecasting and business analytics. Not all data science involves deep learning or LLMs — much of it is good statistics applied to messy real-world data.
Run analyses, build simple models, create dashboards. Learn the domain and the data. Focus on communication skills alongside technical skills.
Own a domain area. Design experiments, build production models, influence product decisions with data. Start mentoring.
Define the data strategy for a product area. Lead complex multi-month projects. Bridge the gap between data team and business leadership.
Shape the company's approach to data-driven decision-making. Define experimentation culture, hiring standards and methodology.
Already working in another field? Here is how your background maps.
The closest jump. You already understand the business side. Learn Python, statistics beyond Excel, and basic ML. Your domain knowledge and stakeholder communication skills are huge advantages.
Learn statistics, experiment design and the data science workflow. Your coding skills will be stronger than most data scientists — but learn to think in hypotheses, not just in code.
Learn Python and SQL. Build projects on real datasets. Your mathematical foundation is strong — focus on applied skills and communication.
