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Top Hybrid Data Science Jobs in Seattle, WA

2 Days Ago
Kirkland, WA, USA
Hybrid
26,000 Employees
193K-337K Annually
Senior level
26,000 Employees
193K-337K Annually
Senior level
Artificial Intelligence • Cloud • HR Tech • Information Technology • Productivity • Software • Automation
Manage a team of Data Scientists to develop machine learning models to enhance business growth opportunities. Define data requirements, extract data from warehouses, build robust predictive models, and educate business teams on model implications. Oversee all aspects from problem identification to solution implementation within an enterprise setting.
Top Benefits:
401-K
401-K Matching
Adoption Assistance
+62 More
10 Days Ago
Bellevue, WA, USA
Hybrid
13,000 Employees
123K-142K Annually
Entry level
13,000 Employees
123K-142K Annually
Entry level
Artificial Intelligence • Healthtech • Professional Services • Analytics • Consulting
As an Applied AI Scientist at ZS, you'll develop advanced algorithms, execute data mining techniques, evaluate datasets and technologies, and contribute to the firm's thought leadership in analytics and machine learning.
Top Benefits:
401-K
401-K Matching
Child Care Benefits
+61 More
10 Days Ago
Bellevue, WA, USA
Hybrid
40,000 Employees
120K-222K Annually
Senior level
40,000 Employees
120K-222K Annually
Senior level
Artificial Intelligence • Digital Media • Machine Learning • News + Entertainment • Software
As a Senior Data Scientist at Warner Bros. Discovery, you will design and build machine learning systems for customer growth, lead the development of scalable ML solutions, promote best practices, and collaborate with cross-functional teams to solve complex problems related to user engagement and retention.
Top Benefits:
401-K
401-K Matching
Adoption Assistance
+72 More

Featured Jobs

6 Days Ago
Seattle, WA, USA
Hybrid
Senior level
Senior level
Sales
The Principal Data Scientist at Magnify will leverage various data sources to create predictive models for revenue expansion and churn, improve model accuracy based on stakeholder input, and collaborate with engineering to enhance data science pipeline infrastructure.
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