Senior Data Scientist, AWS Analytics Engineering

Job summary

Set the technical direction for machine-learning-driven product analytics across AWS services, delivering proactive insights and agentic analytics capabilities that help leaders make product and business decisions. Partner across science, engineering, and service organizations to identify growth drivers and risks, influence senior leaders, establish knowledge organization and governance practices, and mentor data scientists.

Job details

Description Do you want to build science solutions that shape how millions of customers experience AWS products? Do you want to influence AWS investment in Cloud and AI technology, and see your recommendations inform AWS leadership decisions and drive the growth of the AWS business? Do you want to push the boundaries of data science (e.g., statistical modeling, causal inference, econometrics, product growth analytics, and forecasting models) and help democratize how AWS senior leaders access analytics insights using an agentic analytics system? The AWS Analytics Engineering team is at the forefront of leveraging AI/ML technology and infrastructure to redefine how AWS product leaders and teams interact with and derive insights from their product and customer data. Our vision is to use data science methods to enable AWS product teams and business leaders to drive product and revenue growth and create personalized, optimized, and simplified product experiences to delight our customers. We are looking for a customer-focused Senior Data Scientist to develop and deliver science solutions for AWS services. In this role, you will build ML-driven product analytics for AWS Compute (EC2), GenAI & Agents, Database & Analytics, and Storage (S3). You will partner with product managers, business leaders, and senior stakeholders to translate complex business problems into scientific solutions that influence AWS's top line and bottom line. You will analyze product growth insights, understand product growth drivers, and identify business risks that need to be surfaced to leadership. As a Senior Data Scientist, you will analyze business trends and customer insights hands-on, own analyses end to end from problem definition through recommendation, and help raise the bar for scientific rigor on the team. You will mentor junior data scientists through code and methodology reviews and share best practices across the team. You will operate effectively in ambiguous environments, use sound business judgment, show high ownership and a deep understanding of the AWS business, and apply new data science methods at AWS scale. Key job responsibilities Set the technical direction for ML-driven product analytics across AWS Compute (EC2), GenAI & Agents, Database & Analytics, and Storage (S3) Deliver at scale ML-Driven, proactive product analytics for ALL AWS services - initiating and driving GM/VP level conversations Define, own and deliver Knowledge organization best practices for agentic data analytics across AWS Services Build agentic knowledge coverage mechanisms to ensure any service GM/VP/Finance/PM leaders in AWS are able to derive accurate and well covered agentic insights Drive the delivery of cross-service AWS insights, and influence GMs and VPs on product and process changes based on insights delivered. Partner with other science organizations like AWS Central Econ and science (ACES) and IDEAS Science & Economics to drive AWS wide agentic analytics boundaries across organizations and governance mechanisms Work across the AAE Science, Engineering, Core data engineering and service analytics organization to scale science and analytics to all AWS services Basic Qualifications 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience 4+ years of data scientist experience Experience with statistical models e.g. multinomial logistic regression Preferred Qualifications 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience Experience managing data pipelines Experience as a leader and mentor on a data science team Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. As a total compensation company, Amazon's package may include other elements such as sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well-being. We thank all applicants for their interest, however only those interviewed will be advised as to hiring status. CAN, BC, Vancouver - 143,200.00 - 239,100.00 CAD annually

What you’ll do

Set the technical direction for machine-learning-driven product analytics across AWS services, delivering proactive insights and agentic analytics capabilities that help leaders make product and business decisions. Partner across science, engineering, and service organizations to identify growth drivers and risks, influence senior leaders, establish knowledge organization and governance practices, and mentor data scientists.

Requirements

Requires at least 5 years of experience with data querying, scripting, or statistical software and at least 4 years of data scientist experience, including experience with statistical models such as multinomial logistic regression. Preferred qualifications include at least 2 years of data visualization experience, data pipeline management experience, and leadership or mentoring experience on a data science team.

Benefits

• Health Insurance
• Medical Insurance
• Dental Insurance
• Vision Insurance
• Prescription Insurance
• Basic Life and AD&D Insurance
• Registered Retirement Savings Plan (RRSP)
• Deferred Profit Sharing Plan (DPSP)
• Paid Time Off

Listed skills

• SQL · Preferred
• Python · Preferred
• Forecasting · Preferred
• Machine learning · Preferred
• Data visualization · Preferred
• Tableau · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• SQL
• Python
• Statistical Modeling
• Multinomial Logistic Regression
• Causal Inference
• Econometrics
• Product Growth Analytics
• Forecasting
• Machine Learning
• Data Visualization
• Data Pipelines
• Agentic Analytics
• Product Analytics
• Mentoring
• AWS QuickSight
• Tableau

Job areas

• Data & Analytics
• Science & Research
• Technology
• Management & Leadership

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