Product Data Scientist: Payments Analytics & AI Dashboards

TEKsystems; Cupertino (CA); Hybrid; USD 96,000 - 106,000; Full time

Benefits offered by this job

Health insurance

Job summary

TEKsystems is seeking a Data Scientist for its commerce analytics team in Cupertino, CA. This contract role focuses on payment-related initiatives, building scalable dashboards and reporting using SQL, Python, Tableau, and AI-assisted workflows.

The ideal candidate will analyze large datasets, translate insights into business recommendations, and collaborate with product, engineering, and business stakeholders. Hybrid work arrangement with a close-knit analytics function.

Qualifications

• Experience with data analysis, reporting and dashboards using SQL, Python and Tableau.
• Experience leveraging AI tools to automate reporting and build dashboards.
• Ability to translate data insights into actionable business recommendations.
• Experience in financial services or fintech is a nice-to-have.
• Experience analyzing customer behavior or payment datasets is highly preferred.
• Strong communication skills to present insights to stakeholders.

Responsibilities

• Analyze large datasets to generate insights for Tap to Pay product initiatives.
• Build and maintain dashboards and reporting solutions using SQL, Python, Tableau, and AI-assisted workflows.
• Partner with product, engineering, and business stakeholders to support data-driven decisions.
• Continuously explore innovative ways to leverage AI to improve analytics and efficiency.

Skills

Data science

python

SQL

tableau

Pyspark

stakeholder management

Job description

TEKsystems is seeking a Data Scientist for its commerce analytics team in Cupertino, CA. This contract role focuses on payment-related initiatives, building scalable dashboards and reporting using SQL, Python, Tableau, and AI-assisted workflows.

The ideal candidate will analyze large datasets, translate insights into business recommendations, and collaborate with product, engineering, and business stakeholders. Hybrid work arrangement with a close-knit analytics function.