Data Scientist

As a Data Scientist within the Alexa Connections team, you will spearhead the development of end-to-end machine learning and data science solutions designed to enhance intelligent communication experiences across various channels including calling, messaging, and email. You will collaborate closely with product, engineering, and business leaders to transform complex, ambiguous challenges into scalable machine learning models, robust experimentation frameworks, and data-informed product decisions. This role involves designing and deploying advanced ML and statistical models for functionalities such as prioritization, intent detection, and proactive action recommendations. You will analyze extensive datasets and conduct rigorous experiments, including A/B testing and causal analysis, to quantify impact and drive continuous improvements in customer engagement and product performance. Furthermore, you will influence the applied science roadmap and work with global, cross-functional teams to deliver AI-driven solutions that cater to millions of Alexa users.

Key Job Responsibilities

• Collaborate with product, engineering, operations, and security teams to translate intricate business problems into scalable, production-ready data science and ML solutions.
• Manage the complete lifecycle of model development, encompassing exploratory analysis, model design, deployment, monitoring, and ongoing enhancement.
• Define and track key success metrics, perform in-depth analyses, and furnish insights to guide product launches, feature optimizations, and data-driven decision-making.
• Develop data-backed business cases to prioritize science initiatives and illustrate the measurable impact of ML solutions.
• Build ML-powered systems that support critical business functions.
• Lead research and analysis to deepen the understanding of customer interactions with Alexa and elevate the overall customer experience.
• Contribute to the broader science community through mentoring analysts, refining data workflows and tooling, and publishing technical findings in internal and external forums.

A Day in the Life

• Conduct deep dives into business metrics, analyzing data, identifying trends, and reviewing dashboards.
• Write code, including building Python packages, developing SQL queries, and deploying solutions for consumption by Connections experience teams.
• Lead or participate in working sessions with Product Managers to refine problem statements for new initiatives.
• Explore novel features and model architectures, utilizing AWS services, documentation, and continuous self-upskilling in the latest technologies.
• Leverage pre-trained LLMs to construct applications that address business challenges within Connections experiences.
• Meet with Senior/Principal Engineers to align on solution designs.
• Own or co-own Monthly Business Review (MBR) and Weekly Business Review (WBR) documents presented to the Connections leadership team.

Basic Qualifications

• Minimum of 2 years of experience as a Data Scientist.
• Minimum of 3 years of experience with data querying languages (e.g., SQL), scripting languages (e.g., Python), or statistical/mathematical software (e.g., R, SAS, Matlab).
• Minimum of 3 years of experience with machine learning/statistical modeling data analysis tools and techniques, including understanding parameters affecting their performance.
• Minimum of 1 year of experience in creating or contributing to mathematical textbooks, research papers, or educational content.

Preferred Qualifications

• Ph.D. in a Science, Technology, Engineering, or Mathematics (STEM) field.
• Proficiency in machine learning concepts and their application to reasoning and problem-solving.
• Experience in Python, Perl, or another scripting language.
• Experience in an ML or data scientist role within a major technology company.
• Experience in defining and creating benchmarks for assessing Generative AI model performance.
• Experience working on multi-team, cross-disciplinary projects.
• Proven ability to apply quantitative analysis to solve business problems and make data-driven business decisions.
• Strong capability in effectively communicating complex concepts through written and verbal channels.

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