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Top Hybrid Data Science Jobs in Seattle, WA

8+ Job Results
2 Days Ago
Seattle, WA, USA
Hybrid
364,000 Employees
100K-232K Annually
Senior level
364,000 Employees
100K-232K Annually
Senior level
Artificial Intelligence • Professional Services • Business Intelligence • Consulting • Cybersecurity • Generative AI
The AI & GenAI Data Scientist-Manager at PwC will lead teams in developing AI solutions and analytical models, mentor staff, manage project execution, and engage with clients to identify AI opportunities, ensuring high-quality deliverables and adherence to professional standards.
Top Benefits:
401-K
401-K Matching
Adoption Assistance
+66 More
2 Days Ago
Seattle, WA, USA
Hybrid
4,500 Employees
Senior level
4,500 Employees
Senior level
Fintech • Mobile • Software • Financial Services
The Staff Data Scientist role involves leading the Member Data Scientist team to drive growth and engagement strategies through data analysis and machine learning. Responsibilities include mentoring team members, building data pipelines, identifying business opportunities, and collaborating with cross-functional teams to inform product strategy and revenue growth.
Top Benefits:
401-K
Child Care Benefits
Commuter Benefits
+45 More
2 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 algorithms for large dimensional problems, apply statistical and data mining techniques on large datasets, evaluate new data technologies, and contribute to thought leadership through research. You'll work in a collaborative environment focused on transforming global healthcare.
Top Benefits:
401-K
401-K Matching
Adoption Assistance
+63 More

Featured Jobs

2 Days Ago
Seattle, WA, USA
Hybrid
16 Employees
Senior level
16 Employees
Senior level
Logistics
The Principal Data Scientist will work with various data sources to build predictive models that enhance revenue expansion and reduce churn for enterprise software companies. They will collaborate with stakeholders to improve model accuracy, innovate on data science opportunities, and partner with engineering to ensure scalable data science infrastructures.
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