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 · 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.
1,399
Open roles today
$47–$117/hr
Typical pay range
$159k
Median, full-time
8
Fresh in this list
No email, no resume, no sign-up. Save any listing below and you start anonymously.
You're signed in. Saving a listing drops it straight into your pipeline.
01
Open data scientist roles
11 shown of 1,399 · sorted by freshness
Job Title: Data Engineer Location: Los Angeles, CA (Remote/Hybrid) – Locals preferred or anywhere in the US Duration: 6+ months Contract MOI: Phone/Video (MSTeams) Job Description: Python Mongo SQL Server Excellent commu…
$132.26k - $155.6k
career. Try new things, learn new skills and discover what you excel at—all from Day One.Job DescriptionJob Duties -Responsible for big data/ analytics projects that gather and integrate large volumes of data. -Specializ…
$100k - $200k
absolute integrity. East West Bank gives people the confidence to reach further. Overview The Financial Crime Risk Assessment Data Scientist is responsible for supporting the development, maintenance, and enhancement of…
and vendor interfaces alike — including the log, file, and tag-level extraction required where no standard interface exists.Canonical Data Model and Schema Governance: Own the schemas, tag dictionaries, naming convention…
Data Engineer Direct Hire Onsite Pasadena (local are encouraged to apply) W2 Only (No Sponsorship/C2C) We are looking for a Data Engineer to design, optimize, and manage scalable data architecture. In this role, you will…
to help strengthen and scale a modern analytics environment in Los Angeles, California. This position will focus on transforming raw data into dependable business insights through thoughtful modeling, reliable pipelines,…
$110.4k - $165.6k
– both for our audiences and our employees – and aim to leave a positive mark on culture. In This Role You’ll: The Senior Data Engineer should possess a deep sense of curiosity and a passion for building data pipelines,…
Role : Sr. Data Engineer Location: Pasadena, CA Work Arrangement: Hybrid Job Summary We are looking for an... ...enterprise data sources. Collaborate with Data Architects, Data Scientists, BI Developers, and business sta…
Job Overview We are seeking a driven and detail-oriented Data Engineer to build, maintain, and optimize our data infrastructure and... ...~Cross-Functional Support: Partner with data analysts and data scientists to deliv…
$125k - $159k
’s possible in a competitive industry to keep our customers and our culture at the forefront. What You’re Applying For: The Data Platform team manages, supports, and enhances the system that powers Edmunds’ business inte…
02
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.
03
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.
03
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.
06
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
Ten quiet minutes a day
Applying for data scientist jobs in Los Angeles?
Robbi carries this page into your first day: your role, your city, your shift preference. Then it hands you a few small things each morning and keeps the pipeline honest.
Save what looks right here, then let Robbi hand you a few small things each morning and keep the follow-ups honest.
Start with this search
No email, no resume, no sign-up.
Open your tracker
Everything you saved is already there.