Who This Is For Most enterprise data environments were never built to be AI-ready. They were built to survive — cobbled together over years of acquisitions, migrations, and workarounds. The data exists. It's scattered, u…
Engineering & IT · Boston, MA
Data Scientist jobs in Boston, MA
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
3,061
Open roles today
$49–$123/hr
Typical pay range
$166k
Median, full-time
4
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01
Open data scientist roles
12 shown of 3,061 · sorted by freshness
Job Description Job Description About the role: Position Summary We are seeking a Senior Data & Distribution Platform Engineer to support enterprise data, marketing, and distribution applications for a global asset-manag…
focused on different domains - Customer, Loyalty, Search and Browse, Data Integration, Cart. Current overriding priorities are new... ...effectively with stakeholders, data engineers, data scientists, and other cross-fun…
$90k - $140k
, and inspired to grow. We are seeking a Senior Preclinical Data Engineer to support preclinical research and development activities... ...lake. Provide functional, ticket-based user support for scientists using internal…
Position: Senior Incorta Data Engineer Experience Required 6-8 Years Job Summary We are seeking an experienced Senior Incorta Data Engineer to design, develop, and optimize enterprise reporting and analytics solutions us…
achievement are powered by an exceptional team that embodies a true startup mindset. The Platform Engineering Team at ABCorp builds the data infrastructure layer that powers our entire analytics organization. We own the…
$144k - $192k
At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge case…
Job Description Job Description Data Engineer Employment Type: Full-Time, Mid-level Department: Business Intelligence CGS is seeking a passionate and driven Data Engineer to support a rapidly growing Data Analytics and B…
Job Description Job Description hatch I.T. is partnering with Via to find a Senior Data Engineer. See details below: About the Role: An impressive mission requires an equally impressive Senior Data Engineer. As a Senior…
the fastest and most powerful way for design professionals to search, sample, and specify materials. We're looking for a Senior Data & AI Engineer to lead the design, development, and operation of AI agents that power in…
As a Senior Data Engineer, CASM Platform, you will: ~Data Integration, API Development: Integrate diverse cybersecurity data sources using variety of API mechanisms and to standardize and streamline data across the data…
$170k - $240k
and uniquely yours. About the Role We’re seeking talented data engineers to join our founding team, working closely with key... ...ensure data integrity, quality, and compliance. Collaborate with scientists, ML engineers…
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What data scientists earn in Boston
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $49–$68 | $102k–$141k |
| Mid level | $68–$92 | $141k–$192k |
| Senior | $89–$123 | $186k–$256k |
Adjusted for the Boston market from national ranges.
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What employers ask for
The skills these listings keep naming
Python (pandas, scikit-learn)SQLStatistics and A/B testingMachine learning fundamentalsData visualizationExperiment designCommunicating with stakeholdersDomain and product sense
04
Interview questions worth rehearsing
With the thing the interviewer is actually listening for
Walk me through a data project that changed a decision.
Structure it as question, approach, finding, action. Projects that ended in a decision — even 'we did not launch' — beat technically impressive analyses that went nowhere.
How would you design an A/B test for a new feature?
Cover the metric, randomization unit, sample size, and duration, plus a pitfall like peeking or interference. Practical rigor is what is being tested.
Your model performs well offline but poorly in production. Why might that be?
Discuss train/serve skew, data leakage, distribution shift, and feedback loops. Listing several plausible causes and how you would check each is the strong answer.
Explain p-values or confidence intervals to a non-technical stakeholder.
Use plain language and a concrete scenario, and resist overstating certainty. They are testing whether your statistics survive translation.
How do you handle missing or messy data?
First ask why it is missing — the mechanism matters more than the imputation method. Then discuss options and how the choice affects conclusions.
When would you not use machine learning for a problem?
When a rule, a query, or a simple heuristic wins on cost and interpretability. Knowing when ML is overkill signals maturity.
How do you decide which metric a team should optimize?
Talk about proxy versus true goals, gameability, and counter-metrics. A story about a metric that backfired is very effective here.
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Resume tips that move the needle
For data scientists specifically — generic advice costs you here
01
Lead every bullet with the business result — revenue, retention, cost — and put the method second.
02
Specify your stack concretely: Python libraries, SQL dialects, experimentation platforms, and any production ML experience.
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Distinguish shipped work from research; 'model serving 2M users' and 'notebook analysis' are different claims.
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Keep one or two public projects or publications linkable, tailored to the industry you are targeting.
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Name the size and kind of data you worked with — event streams, tabular, text — so teams can map you to their problems.
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Where this role goes
Typical progression
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Junior Data Scientist
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Data Scientist
03
Senior Data Scientist
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Staff Data Scientist
05
Head of Data Science
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