Lead Data Scientist — Global AI & Data Platform

Visa

Bellevue (KY)

Hybrid

USD 180,000 - 288,000

Full time

14 days+

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Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

401(k)

Paid Time Off

Wellness Program

Job summary

Visa leads the way in AI-powered payments and data science, seeking a Lead Data Scientist to drive global data products and AI initiatives. You will partner with engineering, product, cybersecurity, and business teams to deliver scalable, secure analytics that shape the future of digital payments.

As a key engineering leader, you will mentor teams, advance data extraction, modeling, and ETL pipelines, and balance business goals with operational excellence while navigating regulatory and privacy

Qualifications

10+ years of work experience with a Bachelor’s Degree or at least 8 years of work experience with an Advanced Degree (e.g. Masters/ MBA/JD/MD) or at least 3 years of work experience with a PhD.
12+ years with a Bachelor’s Degree or 8-10 years with an Advanced Degree; 6+ years with a PhD as alternative qualifications.
Three (3) years of experience building AI/ML powered models for predictive analytics / insights.
Four (4) years of experience solving data problems using data technologies (e.g., Hadoop, Hive, Kafka, Redis, NoSQL, RDBMS).
Two (2) years of experience designing, implementing, and maintaining ETL pipelines.
Three (3) years of experience with data analysis, metrics building & evaluation.
Experience with Big Data and Analytics using Hadoop, Spark, Flink and MapReduce.

Responsibilities

Provides technical expertise and mentors others to implement extensible, maintainable, and reusable code, defines framework, principles, coding patterns, guidelines, styles, and standard methodologies, and adheres to security requirements for AI and data science.
Develops strategies for and leads team’s efforts to drive efficiencies across data extraction and ensure data quality and completeness using data wrangling, complex data modeling, and AI.
Ensures adherence to data management principles, governance, process, and tools to maintain data quality across products.
Advises on technical specifications during discussions with collaborators to identify business needs and upstream/downstream dependencies.
Identifies complex trends across data sources and defines roadmap for software upgrades and security patches.