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# Sr. Applied Scientist - AI Velocity Team, Applied...

- **Company:** Amazon.com, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $183,800.0 - $248,700.0
- **Contract:** Permanent contract
- **Skills:** Microsoft Excel, A/B Testing, Artificial Intelligence, Amazon S3, Data Analysis, Continuous Integration, Data Mining, Software Design Documents, Python (Programming Language), Machine Learning, Tensorflow, Software Deployment, SQL Databases, Unstructured Data, Amazon Connect, Macros, Pytorch, Deep Learning, Scikit Learn, Information Technology, Xgboost, Machine Learning Operations
- **Published:** July 19, 2026
- **Apply:** https://www.juju.com/job/00000000ghqrmv

## About the Role

Master's degree in engineering, statistics, computer science, mathematics, or a related quantitative field

 - 5+ years of quantitative and qualitative data science/business intelligence with significant business impact experience

 - 3+ years of machine learning, statistical modeling, data mining, and analytics techniques experience

 - PhD, or PhD and 4+ years of designing experiments and statistical analysis of results experience

 - Experience in A/B testing

 - Proficiency in Python and SQL; experience with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost

 - Track record of delivering end-to-end data science solutions from problem definition through production deployment

Preferred Qualifications

 - PhD in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science

 - Experience with AI/ML technologies

 - Knowledge of AWS platforms such as S3, Glue, Athena, Sagemaker

 - Experience working directly in customer implementations

 - Experience building and managing financial models for business forecasting and problem solving, or experience in Excel (macros, index, conditional list, arrays, pivots, lookups)

 - Experience building MLOps workflows (CI/CD for models, feature stores, model registries) or real-time inference systems

 - Publications at peer-reviewed conferences or journals (NeurIPS, ICML, KDD, ACL, EMNLP, etc.)

 - Experience with contact center, customer experience, or telecommunications data

 - Proven ability to influence without authority and communicate effectively across organizational boundaries

## Description

Are you passionate about using data science to transform how businesses understand and optimize customer interactions at scale? Do you want to build the models and analytics that power the next generation of AI-driven customer experiences while working directly with customers to accelerate production deployments?

As a Senior Applied Scientist within the Applied AI Solutions team, you will collaborate across AI Velocity Teams (AIVT), enabling multiple customer engagements simultaneously. You will lead data science initiatives that span the full lifecycle - from identifying high-value business problems and formulating hypotheses, through rigorous experimentation and modeling, to deploying production-grade solutions that serve thousands of customers. You will bring deep expertise in statistical inference, machine learning, and experimental design to drive measurable impact across Amazon Connect's analytics products and broader Connect AI initiatives.

A critical dimension of this role is working directly with customers during production pilots to accelerate time-to-value. You will partner with Applied AI Solutions Architects and Customer Success Specialists to design, build, and deploy AI solutions in customer environments during fixed deployment cycles. You will enable field teams with data-driven insights, reusable analytical assets, ROI tools, and scalable tooling that accelerate customer engagements and solution delivery. Your work will directly influence customer decisions to adopt Connect Customer AI by quantifying business outcomes and demonstrating measurable value.

You will operate with significant autonomy, owning the scientific direction of your projects while collaborating with applied scientists, software engineers, product managers, technical, and business stakeholders. You will be expected to identify the right methodology for each problem - whether that's a classical statistical approach, a modern deep learning technique, or a novel combination - and communicate your findings clearly to both technical and non-technical audiences. This role spans Connect AI initiatives including conversational analytics and agentic AI capabilities, offering the opportunity to pioneer data science approaches that scale intelligent analytics worldwide.

Key job responsibilities

 - Design, develop, and deploy statistical models and machine learning pipelines to drive product improvements and business decisions

 - Work directly with customers during production pilots to design, build, and deploy AI solutions that demonstrate measurable business value

 - Design and execute A/B experiments and causal inference analyses to measure the impact of new features and model changes on customer outcomes

 - Build ROI models and business case tools that quantify the value of Connect Customer AI for existing customers transitioning from Connect Customer Basic

 - Develop and maintain forecasting systems for demand prediction, capacity planning, and workforce optimization

 - Develop and apply NLP and generative AI techniques to extract insights from structured and unstructured data at scale

 - Partner with applied scientists and software engineers to productionize models, ensuring reliability, monitoring, and operational excellence

 - Enable AI Velocity teams with reusable analytical assets, diagnostic notebooks, and scalable tooling that accelerate customer engagements

 - Build benchmarking studies and optimization frameworks that demonstrate value across customer cohorts

 - Own success metrics and create mechanisms to measure model performance, adoption, and business impact

 - Communicate findings and technical trade-offs to senior leadership and customer executives through written documents (6-pagers, science reviews) and presentations

 - Operate as a shared resource across 2-3 AIVT teams simultaneously, providing data science expertise across multiple customer engagements

A day in the life

 - Start the morning on a call with the AI Velocity Teams preparing for a strategic customer engagement - reviewing the analytical assets and dashboards you've built, walking through how to interpret model outputs, and tailoring recommendations to the customer's contact center environment

 - Join a customer working session where you're deploying a production pilot - analyzing their historical contact data, building demand forecasting models, and demonstrating how AI optimizations will reduce their cost per serviced contact while improving customer experience metrics

 - Dive into a deep analysis triggered by AIVT field feedback - a large enterprise customer is seeing unexpected patterns in their contact data, and you're pulling together multi-source data to isolate root cause and build a reusable diagnostic notebook the AIVT team can leverage for similar cases

 - Participate in a Conversational Analtyics science review, presenting your A/B test results on a new sentiment classification approach and discussing trade-offs between model accuracy and inference latency with the engineering team

 - Spend the afternoon building a reusable ROI calculator that field teams can use across customer engagements - packaging your economic models with configurable parameters so teams can quickly quantify the value of Connect Customer AI for different customer profiles and usage patterns

 - Collaborate with AI Architects and Customer Success Specialists across your three active AIVT engagements, providing data science guidance on model selection, evaluation frameworks, and success metrics for each customer's unique use cases

 - Wrap up by reviewing a design document for an agentic AI feature that will use conversation analytics to automatically surface coaching recommendations for contact center supervisors, providing feedback on the evaluation methodology and success metrics


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