Senior Data Scientist, AWS Analytics Engineering
- Vancouver, BC, Canada
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Job details
- Experience
- 4+ years of data scientist experience
- Posting language
- English
- Posted
Original job description
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
More about this job
Responsibilities
The Senior Data Scientist will develop and deliver ML-driven product analytics solutions across various AWS services to influence leadership decisions. They will also mentor junior team members and define best practices for agentic data analytics.
Requirements
Candidates must have at least 5 years of experience with data querying and scripting languages, along with 4 years of professional data scientist experience. Proficiency in statistical models such as multinomial logistic regression is required.
Skills
- Data science
- SQL
- Python
- Statistical modeling
- Causal inference
- Econometrics
- Forecasting models
- Machine learning
- Product analytics
- Data visualization
- AWS QuickSight
- Tableau
- R
- Data pipelines
- Leadership
- Mentorship
Benefits
- Health insurance
- Medical insurance
- Dental insurance
- Vision insurance
- Prescription insurance
- Basic life insurance
- AD&D insurance
- Deferred Profit Sharing Plan
- Paid time off
- Restricted stock units
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Science & Research
- Software
- Engineering
Keywords
- Data Science
- AWS
- Machine Learning
- SQL
- Python
- Statistical Modeling
- Causal Inference
- Econometrics
- Forecasting
- Product Analytics
- Cloud Computing
- Artificial Intelligence
- Data Visualization
- AWS QuickSight
- Tableau
- Data Pipelines
- Leadership
- Mentorship
- Agentic Analytics
- Multinomial Logistic Regression
- R
- SAS
- Matlab
- Product Growth
- Business Intelligence