critical infrastructure that supports the digital age and shapes the spaces where people work, connect, and thrive. From high-performance data centers driving the future of AI to dynamic commercial environments, your wor…
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.
1,615
Open roles today
$49–$123/hr
Typical pay range
$166k
Median, full-time
11
Fresh in this list
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01
Open data scientist roles
12 shown of 1,615 · sorted by freshness
$108.75k - $180k
underwriting, and financial forecasting activities. This position is responsible for designing, developing, maintaining, and enhancing data workflows, analytical solutions, and credit loss models that support regulatory…
$145k - $165k
OverviewAs a Lead of Analytics Engineering at Avison Young Technologies, you will lead the strategy and execution of our proprietary data products and models that empower commercial real estate decision makers across inv…
$165k - $185k
About the JobSenior Data EngineerOur client, a highly regarded investment management firm in Boston, is investing heavily in its next-generation cloud data platform and is looking for a Senior Data Engineer to help drive…
$71 - $80 per hour
DescriptionKforce has a client in need of a Senior Data Quality & Snowflake Migration Engineer in Boston, MA.Responsibilities:* Design and implement data quality and validation processes for large-scale cloud data migrat…
$87.7k - $139.7k
experienced Senior High Voltage Electrical Engineer to lead the design and engineering of critical electrical infrastructure for hyperscale data centers and mission-critical facilities. This role provides technical leade…
$57 - $64 per hour
document, track, and support resolution of system and security-related issues* Support cybersecurity initiatives focused on insider risk, data protection, incident response, and security monitoring* Assist with the devel…
Managing Director, Data Engineering & AIWho You'll Work WithAs a Managing Director in Slalom's Data & AI practice, you will lead the growth of our Data Engineering & AI business by helping clients modernize their data fo…
Company DescriptionOverviewEpsilon’s Data Science & AI practice within Analytics Services team is seeking a Senior Staff Data Scientist to lead the delivery of advanced analytics and AI solutions that drive measurable bu…
We are looking for a skilled Data Engineer to join a 100% remote contract to hire position. This role focuses on developing and maintaining data warehouse integration processes, working closely with technical teams and b…
$80.46k - $142.72k
deliver more impact together.Role description:As an Electrical Engineer you will lead the electrical discipline of multiple concurrent data center projects through pursuit, proposal, design, and construction phases. You…
We are seeking an experienced Snowflake Data Engineer with strong expertise in Snowflake, Python, SQL, Snowpark, and ELT pipeline development. The ideal candidate will have a strong background in data engineering, data w…
02
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.
05
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.
04
Keep one or two public projects or publications linkable, tailored to the industry you are targeting.
05
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
01
Junior Data Scientist
02
Data Scientist
03
Senior Data Scientist
04
Staff Data Scientist
05
Head of Data Science
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