Data Scientist

Data Scientist

Imagine what you can do here. Apple is a place where extraordinary people gather to do their lives best work. Together we create products and experiences people once couldn't have imagined, and now, can't imagine living without. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do.
APPLE INC has the following available in Austin, Texas. Utilize Python (Pandas, NumPy) for transactional fraud risking to generate and enhance business intelligence solutions for partner teams. Adhere to best practices in big data analytics by leveraging PySpark, SQL, Git and job orchestration frameworks such as Airflow. Demonstrate expertise with visualization tools (Tableau, Matplotlib, Superset, etc.) for reporting and presenting fraud impact analysis. Design experiments and analytical processes to detect opportunities in fraud prevention workflows and reduce risks including identity theft, account takeover and financial fraud. Work collaboratively to mitigate fraud risk while balancing customer experience, business objectives, operational efficiency, legal and technological requirements. Leverage domain knowledge to evaluate and improve risking in areas such as identity verification and authentication methods within Apple Wallet products. Present and communicate impact analyses and technical topics to non-technical to internal and external program partners and leadership. Work with internal and external partners by providing actionable insights and decision support for fraud risking.
Bachelor’s degree or foreign equivalent in Mathematics, Business Analytics, Data Science, or a related field and 2 years of experience in the job offered or related occupation.

Experience and/or education must include:

1. Using Python to conduct large-scale analyses and risk assessments.
2. Utilizing visualization tools including Tableau, Power BI, or Superset, and Matplotlib to create interactive dashboards and reports to support decision-making processes in analytics teams.
3. Implementing data analysis skills to conduct deep dives by applying statistics to generate clear actionable insights.
4. Utilizing data stores such as Apache Iceberg, Snowflake, or Hadoop, and employing SQL and distributed analytics engines like PySpark, to perform complex queries and large-scale data transformations efficiently.
5. Communicating complex analyses to executive leadership and technical audience.
6. Collaborating with cross functional teams, ranging from engineering and finance to business.
7. Implementing identity authentication and verification methods, with knowledge of industry identity standards and best-practice guidelines.
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