Data Scientist
Data scientists pull patterns out of messy company data to help leaders make decisions or build prediction models. Most of the job is cleaning data and explaining results, not the glamorous AI stuff.
What Tuesday looks like
You log on at 9, skim Slack, and join a standup where five people talk for 15 minutes about things half of you don't need to hear. Then you go back to the dataset you've been wrestling with for three days — customer churn data that has duplicate rows, weird nulls, and timestamps in two different formats. You write SQL, run it, find another problem, write more SQL. Around 11 a product manager messages asking if you can 'just pull a quick number' for a meeting tomorrow. It's not quick. You context-switch for an hour. After lunch you finally get back to the churn model, train a version, and the accuracy looks suspicious — probably data leakage. You'll debug it tomorrow. You spend the last hour making a slide explaining last week's results to non-technical execs in terms they won't misread. You log off at 6. Nothing shipped today.
Career profile
Career shape
Tap or hover each point to explore a dimension
In the landscape
Tap or hover any dot to identify a career
Salary range
$85K
Entry
$108K
Median
$142K
Senior
$65K floor
$184K ceiling
10-yr growth
+35%
9/10 exposure
Reward profile
3 quick questions to see how this career fits the way you work.
What school costs — and when it pays off
Bachelor's degree · Four years at a public university. Costs here use the cheaper in-state rate.
The chart shows your annual salary over time alongside the annual loan repayment. The shaded band at the bottom is what goes to the loan each year — when it disappears, your full salary is yours.
School cost fully covered by year 9, with strong earnings well beyond that.
Entry-level salary
$85K
25th percentile — what most people start at
Experienced salary
$142K
75th percentile — after ~10 years in the field
School & training cost
$80K
+ $29K interest over 10 yrs
Loan paid off
Year 14
$910/mo for 10 years
First year of work
After loan's paid (yr 14)
Salary range reflects 25th–75th percentile nationally, growing from entry-level to experienced over 10 working years. School costs are national averages — yours will vary. Loan assumes you borrow the full amount at 6.54% interest, repaid over 10 years. Monthly figures are pre-tax.
The first years
Year 1–2: Junior Data Scientist / Analyst
You start around $75K–$95K and quickly realize 'data science' mostly means SQL, spreadsheets, and explaining to a product manager why their number is different from finance's number. You spend most of your time pulling data for other people's questions and cleaning datasets nobody warned you would be this broken. The ML coursework you did in college barely shows up — you're proving you can be trusted with simple stuff before anyone hands you a model.
Year 2–4: Data Scientist
You're now owning projects end-to-end — defining the question with a stakeholder, building the dataset, training a model, and presenting results. Pay climbs to around $105K–$130K. You learn that the hardest part isn't the model, it's getting people to actually use it, and that half your 'models' end up being a SQL query and a dashboard because that's what the business needed. You start picking up tools like dbt, Airflow, or cloud platforms because nobody else is going to set up your pipelines.
Year 4–5: The Fork
Around here you're senior enough that the path splits and you have to pick. You're competent, paid decently ($130K–$160K), and the AI wave is reshaping what 'data scientist' even means — a lot of the analysis work you used to do can now be done faster with LLMs and AI tools, so coasting isn't really an option.
Decision point
Do you go deep on machine learning engineering (more coding, MLOps, production systems, working closer to software engineers), lean into analytics and become the person who turns data into business strategy (more communication, less modeling), or move toward management? Each path uses different skills and the people you'd be competing with are different. Picking wrong isn't fatal, but staying a generalist past this point usually means your salary stalls while specialists pass you.
Year 5–7: Senior Data Scientist (or ML Engineer / Analytics Lead)
Title is 'Senior' and pay is roughly $150K–$200K depending on company and location. You're spending less time in notebooks and more time in design docs, code reviews, and meetings about what to build and why. Juniors ask you questions, execs ask you for numbers, and you're the person who has to say 'no, that model won't work for what you want.' The work is more interesting but also more political, and you're judged on business impact now, not technical cleverness.
The path in
Statistics · Computer Science · Data Science · Mathematics · Economics
Most data scientists have a STEM bachelor's, with strong coursework in statistics, programming (Python/R), and linear algebra. Many entry-level 'data scientist' roles actually want a master's now, so plan on either a strong portfolio or grad school.
Data Science · Statistics · Analytics · Machine Learning
A master's is increasingly the standard credential for 'data scientist' titles, especially at larger companies. Many people start as data analysts after their bachelor's, then get a master's part-time while working.
Data Science Bootcamp · Analytics Bootcamp
Bootcamps work best for career switchers who already have a quantitative bachelor's — pure bootcamp grads with no STEM background struggle to land data scientist roles. Most bootcamp grads start as analysts, not full data scientists.
Online courses (Coursera, fast.ai) · Kaggle competitions
Possible but hard — you'll need a serious portfolio (Kaggle rankings, GitHub projects, published analyses) to get past resume screens. Easier to break in as an analyst first, then transition.
Known for this field
Top-ranked program for both stats and ML. Strong industry pipeline to big tech and quant firms.
Heart of Silicon Valley — unmatched access to internships at top tech companies and AI startups.
One of the first dedicated undergrad data science majors. In-state tuition is a strong value.
Strong stats department with established data science undergrad track and active recruiting from major employers.
Top CS program with affordable in-state tuition. Their online MCS-DS master's is a popular next step.
OMSA is one of the cheapest legitimate master's options (~$10K total) — common path for working analysts.
Affordable two years, then transfer to UC Berkeley or UC Davis for the data science degree.
One of the more established data science bootcamps with mentorship and a job guarantee. Best for people who already have a quantitative bachelor's.
Related paths
Machine Learning Engineer
Data scientists who get deeper into model building and production code often become ML engineers. The roles share statistics and Python skills but ML engineering is more software-focused.
Physician
Both rely on pattern recognition and evidence to make high-stakes decisions, and healthcare data science is a fast-growing crossover field.
Financial Analyst
Both careers rely heavily on analyzing data to drive decisions. Data scientists use code and machine learning, while financial analysts focus on money and markets.
Product Manager
Data scientists who like influencing strategy often become product managers, since they already understand metrics, experiments, and user behavior.