Role Description As a Lead Data Scientist, you will combine hands-on expertise, technical leadership, and product thinking to drive the development of scalable ML solutions. You will play a key role in: ~Ensuring what we…
Engineering & IT · Remote
Remote Data Scientist jobs
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
56,569
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$38–$96/hr
Typical pay range
$130k
Median, full-time
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01
Open data scientist roles
12 shown of 56,569 · sorted by freshness
Role Description We are seeking an experienced Lead Data Engineer with strong Terraform expertise to design, build and maintain scalable data solutions on Google Cloud Platform. In this role, you will lead the developmen…
Role Description The data and AI team is a critical function within Business Technology. Our mission is to enable integrated data layers... ...data engineers, AI engineers, architects, analysts, and data scientists. We a…
$170k - $190k
Role Description As a Lead Data Engineer, you will serve as a senior technical leader within the Data Engineering organization, helping define the architecture, engineering standards, and best practices that support Walk…
Role Description 弊社のトップクライアントであるLINEヤフーグループ企業にて、月間3,000万人以上が利用する大規模サービスのデータ基盤構築をリードする Data Engineerを募集しています。独自の検証データや大規模なユーザー行動データを活用し、データドリブンな意思決定、AI活用、プロダクト成長を支える重要なポジションです。 本ポジションでは、同社が保有する独自の「検証データ」と月間3,000万人以上のユーザーか…
$160.56k - $193k
Role Description Lead Data Engineer, Fortune Brands Innovations Group, Inc., Deerfield, IL. ~Administer a cloud data warehouse platform (e.g., Snowflake or similar), including roles/permissions, performance tuning, secur…
needs. ~Start Date: Immediate need — target start within 1 week. ~Location: remote from U.S. We are seeking a Senior SQL Data Developer with deep SQL expertise, Oil & Gas industry experience, and hands-on experience supp…
$90k - $135k
Role Description As a Data Engineer at Wellbe you will play a pivotal role in collecting, processing, and analyzing large datasets... ...You will collaborate with cross-functional teams, including data scientists and bus…
$140k - $160k
Role Description As a Lead Data Scientist at Janssen Supply Group, LLC, you will: ~Perform Advanced System Simulation and Optimization Modeling supporting design of large and complex pharmaceutical and medical device sup…
$91.9k - $144.1k
Role Description We’re seeking a Mid-Level Data Engineer/Analyst to independently design, build, and optimize data pipelines and analytics... ...Snowflake and Databricks, and collaborate with analysts, data scientists, a…
Role Description Tigunia is seeking a technically focused Business Intelligence Analyst with a specialization in data engineering and warehouse modeling. This role is ideal for someone who enjoys building scalable data i…
Role Description We're looking to hire a Data Migration Engineer to own the technical execution of data migrations that move customers from legacy systems into gaiia. You'll join the Migration Solutions team, which owns…
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What data scientists earn remotely
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $38–$53 | $80k–$110k |
| Mid level | $53–$72 | $110k–$150k |
| Senior | $70–$96 | $145k–$200k |
National ranges — remote pay usually tracks the employer's base market.
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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.
04
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
01
Junior Data Scientist
02
Data Scientist
03
Senior Data Scientist
04
Staff Data Scientist
05
Head of Data Science
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