Amazon Development Centre Canada ULC

E-Commerce

AppliedScientistManager,TaxEngine

CA$222–372k Vancouver, British Columbia, Canada FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Manager candidates.

The Brief

“Applied Scientist Manager, Tax Engine at Amazon Development Centre Canada ULC. Skills: Data Science, Machine Learning, Data Engineering, Business Intelligence. Design and implement data solutions. Develop and deploy machine learning models”

What You'll Achieve.

Improve business insights; Drive data-driven decisions; Enhance product features; Optimize operational efficiency

Industry & Context.

E Commerce
Problems you'll solve

Root cause analysis; Analytical thinking; Troubleshooting; Data-driven decision making

What They're Looking For.

Must Have

3+ years of experience in data science or related field, Bachelor's degree in Computer Science, Statistics, Mathematics, or related quantitative field, Proficiency in SQL, Experience with Python or R for data analysis and modeling

Nice to Have

Master's degree or PhD in a quantitative field, Experience with cloud platforms (AWS, GCP, Azure), Experience with ML frameworks (scikit-learn, TensorFlow, PyTorch), Experience with BI tools (Tableau, Power BI, Looker), Experience with data warehousing solutions (Snowflake, BigQuery, Redshift), Experience with data pipeline orchestration tools (Airflow, dbt), Experience with large-scale data processing frameworks (Spark)

What You'll Do.

Design and implement data solutions

Develop and deploy machine learning models

Build and maintain data pipelines

Perform complex data analysis

Create and maintain BI dashboards

Collaborate with engineering teams

Mentor junior data scientists

Stay current with industry trends

How You'll Work.

Team & Collaboration

Cross-functional teams; Engineering teams; Product managers; Business stakeholders

Communication Scope

Technical presentations; Stakeholder communication; Documentation

Process & Methodology

Agile methodologies, Project planning, Roadmap development

Full Job Description

Amazon ‘s Tax engine organisation is looking for a passionate and innovative science leader to take its science initiatives to new heights. Amazon Tax Engine platform backs all of the orders placed across Amazon e-commerce. We serve Amazon customers and sellers by correctly computing and collecting the tax amounts when an Amazon order is placed globally. We are responsible for correctly attributing products to the correct Tax categories applicable for the specific country, state and county, providing core calculation services that calculate taxes (sales tax and VAT) for all Amazon sales, physical and digital. Our challenges include staying on top of the complex and ever-changing global tax legislations as well as computing calculations correctly and quickly, thousands of times a second, with accuracy close to 100%. We use language models at scale for Tax classification of the diverse products in Amazon catalogue We have a growing portfolio of science problems that includes balance of predictive and generative AI -including language comprehension, causal reasoning and active learning. Key job responsibilities - Manage and mentor a talented team of senior Appleid Scientists and SDEs. - Improve process and methodologies pertaining to deliverables of the team. - Strike the right balance between experimentation and delivery for sustained impact and long term gains. - Partner with stakeholders and customers to build roadmap for new products and services. Basic Qualifications: - 3+ years of scientists or machine learning engineers management experience - Knowledge of ML, NLP, Information Retrieval and Analytics - 3+ years of building machine learning models or developing algorithms for business application experience Preferred Qualifications: - Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers - Experience communicating with users, other technical teams,

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