WPC Apple Card Data Scientist

Cupertino, CA, US • Posted 19 hours ago • Updated 6 hours ago
Full Time
On-site
Fitment

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Job Details

Skills

  • Collaboration
  • IDEA
  • Management
  • Product Marketing
  • Product Strategy
  • Roadmaps
  • Payments
  • Banking
  • Credit Risk
  • Economics
  • Computer Science
  • Data Science
  • Financial Modeling
  • Business Analysis
  • Data Analysis
  • Valuation
  • Modeling
  • Python
  • Pandas
  • Relational Databases
  • SQL
  • Amazon Redshift
  • Cloud Computing
  • Snow Flake Schema
  • Databricks
  • Workflow
  • Extract
  • Transform
  • Load
  • Data Visualization
  • Communication
  • Organized
  • Econometrics
  • Statistics
  • Machine Learning (ML)
  • Analytics
  • Mathematics
  • Operations Research
  • Industrial Engineering
  • Credit Cards
  • Network
  • Brand
  • Finance

Summary

Apple is a place where extraordinary people gather to do their best work. Together we craft products and experiences people once couldn't have envisioned - and now can't imagine living without. If you're excited by the idea of making a real impact and joining a team where we pride ourselves in being one of the most diverse and expansive companies in the world, a career with Apple might be your perfect job.

Description

The Wallets, Payments, and Commerce (WPC) team at Apple is looking for a full-stack Data Scientist who is passionate about crafting and implementing data solutions that have a direct and measurable impact on Apple customers. In this role, you will help chart the future direction of Apple Card by identifying and investigating new opportunities, projecting future results, and ensuring the business can execute against its long-term strategy.

You will partner directly with product, marketing, finance, and credit teams, working cross-functionally to deliver results, as well as working with external partners such as issuing banks to help deliver on our product strategy and roadmap.

You should have experience and subject matter expertise in financial modeling, payments, banking, credit, risk operations, data science, and finance. Previous experience working in the credit card industry at an issuer, network or co-brand partner is highly preferred. Experience with a variety of financial products is a plus.

Minimum Qualifications

Minimum of bachelor's degree in engineering, data science, economics, statistics, computer science, or related quantitative field

Minimum of 8 years of experience with a proven track record of success in data science roles focused on financial modeling, business analysis, finance, and data analysis

Deep understanding of product valuation modeling and marginal decisioning

Expert data wrangler in Python (e.g., Pandas, Polars) with experience working with relational databases, including SQL, and large-scale distributed systems such as Redshift

Proficient in cloud data platforms such as Snowflake and Databricks

Strong experience in building scalable production ready workflows, end-to-end ETL pipelines and predictive models that enable data-driven decisions

Exceptional data visualization and storytelling abilities, capable of translating complex analyses into clear, executive-ready insights

Excellent written and verbal communication skills, adept at translating technical results into clear, compelling narratives for non-technical and executive audiences

Highly organized, self-driven, and effective at prioritizing and delivering multiple initiatives under tight timelines

Preferred Qualifications

Advanced degree in Applied Econometrics, Statistics, Machine Learning, Analytics, Mathematics, Operations Research, Industrial Engineering, or related field preferred.

Previous experience working in the credit card industry at an issuer, network or co-brand partner is highly preferred

Experience with a variety of financial products
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: 90733111
  • Position Id: e4749e5d24f62140f6ee8cc3b336bb76
  • Posted 19 hours ago
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