Principal Data Scientist, WWPS ProServe Security Clearance

Position: Principal Data Scientist, WWPS ProServe with Security Clearance
Description This position requires that the candidate selected be a US Citizen and currently possess and maintain an active Top Secret security clearance. The Amazon Web Services Professional Services (Pro Serve) team seeks an experienced Principal Data Scientist to join our Pro Serve Shared Delivery Team (SDT). In this role, you will serve as a technical leader and strategic advisor to AWS enterprise customers, partners, and internal AWS teams on transformative AI/ML projects.

You will leverage your deep technical expertise to architect and implement innovative machine learning and generative AI solutions that drive significant business outcomes. As a Principal Data Scientist, you will lead complex, high-impact AI/ML initiatives across multiple customer engagements. You will collaborate with Director and C-level executives to translate business challenges into technical solutions. You will drive innovation through thought leadership, establish technical standards, and develop reusable solution frameworks that accelerate customer adoption of AWS AI/ML services.

Your work will directly influence the strategic direction of AWS Professional Services AI/ML offerings and delivery approaches. Your extensive experience in designing and implementing sophisticated AI/ML solutions will enable you to tackle the most challenging customer problems. You will provide technical mentorship to other data scientists, establish best practices, and represent AWS as a subject matter expert in customer-facing engagements.

You will build trusted advisor relationships with customers and partners, helping them achieve their business outcomes through innovative applications of AWS AI/ML services. The AWS Professional Services organization is a global team of experts that help customers realize their desired business outcomes when using the AWS Cloud. We work together with customer teams and the AWS Partner Network (APN) to execute enterprise cloud computing initiatives.

Our team provides a collection of offerings which help customers achieve specific outcomes related to enterprise cloud adoption. We also deliver focused guidance through our global specialty practices, which cover a variety of solutions, technologies, and industries. Key job responsibilities
Architecting and implementing complex, enterprise-scale AI/ML solutions that solve critical customer business challenges Providing technical leadership across multiple customer engagements, establishing best practices and driving innovation Collaborating with Delivery Consultants, Engagement Managers, Account Executives, and Cloud Architects to design and deploy AI/ML solutions Developing reusable solution frameworks, reference architectures, and technical assets that accelerate customer adoption of AWS AI/ML services Representing AWS as a subject matter expert in customer-facing engagements, including executive briefings and technical workshops Identifying and driving new business opportunities through technical innovation and thought leadership Mentoring junior data scientists and contributing to the growth of AI/ML capabilities within AWS Professional Services Basic Qualifications - Bachelor's degree in engineering, statistics, computer science, mathematics, or a related quantitative field
- Experience in leading large-scale, technical or engineering programs with a proven record of thought leadership, business case development, realizing customer benefits, and successful program completion
- 11+ years of building machine learning models or developing algorithms for business application experience
- Deep expertise in Python and relevant ML frameworks (Tensor Flow, PyTorch, Hugging Face, etc.)
- Current, active US Government Security Clearance of Top Secret or above

Preferred Qualifications - PhD in computer science, machine learning, engineering, or related fields, or MS degree
- AWS Professional level certification

- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference life cycles, and optimization of model execution, or experience with vLLM, SGLang, Tensor

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