Senior Data Engineer Core Data Platform Location: Burbank, California-Onsite Job Type: Full-Time, Permanent About... ...data lake architectures. Collaborate closely with Data Scientists, Analytics teams, Product Managers…
Engineering & IT · Los Angeles, CA
Data Scientist jobs in Los Angeles, CA
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
3,287
Open roles today
$47–$117/hr
Typical pay range
$159k
Median, full-time
4
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01
Open data scientist roles
11 shown of 3,287 · sorted by freshness
Data Engineer Location: Orlando, FL or Los Angeles, CA Must be willing to relocate/work onsite (2 weeks' notice from time of offer accept) Zero exceptions will be made, as this is corporate policy - no delayed remote sta…
$130k - $170k
car manufacturers. Our success rate is unmatched by any other Firm. Strategic Legal Practices is seeking a self-starting Senior Data Engineer to help shape how a high- impact litigation firm leverages data. In this role,…
$200k - $225k
Job Description Job Description Apply now: Senior Data & AI Engineer, location is 100% onsite in Santa Monica, CA. The start date is ASAP for this permanent position. Job Title: Senior Data & AI Engineer Location-Type: 1…
Job Description Job title: Senior Data Engineer Experience: 8-15 Years Location: Glendale, USA Job Type: Full-time... ...data ecosystem. Collaborate with other data engineers, data scientists, and cross-functional teams.…
Job Description Job Description We're working with a growing home furnishings retailer that's modernizing its data infrastructure - think legacy SQL Server systems evolving into cloud-native pipelines feeding BI and emer…
$105k - $200k
Description Description We are currently seeking highly motivated Data Engineers at various levels. This role will report to the... ...Engineering and work closely with data analysts, data engineers, data scientists, and…
$141.9k - $190.3k
Entertainment and ESPN Product & Technology is a global organization of engineers, product developers, designers, technologists, data scientists, and more – all working to build and advance the technological backbone for…
$110.4k - $165.6k
moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture. Overview As a Senior Data Engineer, you will play a pivotal role in driving our data strategy and standard met…
$130k - $150k
Apply now: Lead Data Engineer / Delivery Lead, location is in LA. The start date is ASAP for this contract position. Job Title: Lead Data Engineer / Delivery Lead Location-Type: LA Start Date Is: ASAP Duration: Contract…
$100k - $120k
Job Description Job Description Local Candidates Only - Onsite Role About the Role As a Power BI Developer / Senior Data Analytics professional, you will be responsible for transforming complex datasets into actionable i…
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What data scientists earn in Los Angeles
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $47–$64 | $98k–$134k |
| Mid level | $64–$88 | $134k–$183k |
| Senior | $85–$117 | $177k–$244k |
Adjusted for the Los Angeles 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.
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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
01
Junior Data Scientist
02
Data Scientist
03
Senior Data Scientist
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Staff Data Scientist
05
Head of Data Science
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