Junior Data Scientist

Most days mix supervised analysis work with structured learning — pulling and cleaning data, running exploratory analyses, building basic models under direction, supporting senior scientists on larger projects, and learning the tools and patterns that the team uses (Python, R, SQL, notebooks, ML libraries). You're often working in tech, finance, healthcare, retail, or consulting, and the team's mission — research, product analytics, ML engineering — shapes early exposure.

What tends to be harder than people expect is how much of data science work is data plumbing and stakeholder management, not modeling. Real datasets are messier than tutorials suggest, stakeholders interpret results differently than you intend, and prioritization shifts. Mentorship quality, infrastructure maturity, and how seriously the org takes experimentation shape early growth dramatically.

People who tend to thrive here are statistically curious, fluent in code, comfortable with ambiguity, and willing to learn from senior scientists. If you want pure ML research, that lives in research roles. If you like building a foundation in applied data work, the early years open paths toward senior data scientist, ML engineer, or specialty roles.