Senior Data Scientist, AI Solutions Engineering - Client Services

Visa•Austin, TX

6d•$133,300 - $213,400•Hybrid

About The Position

The Sr. Data/AI Solutions Engineer role will be responsible for driving data-driven decision making across the organization. They will collaborate with business stakeholders, understand their problems, and design data-based solutions for them by making data available from new sources, build robust data models, creates and optimizes data enrichment pipelines, and provides engineering support to specific projects. This is a technical role that acts as a force multiplier and other data users across Client Services. This role requires hands-on expertise on Data Engineering and AI/ML solutions. The role requires hands-on experience with large-scale data sets, building scalable models, and applying machine learning and statistical techniques to solve business problems. The position involves collaborating with cross-functional teams, translating analytics output into actionable recommendations, and communicating complex concepts to diverse audiences. All roles require AI fluency, including the ability to work with emerging technologies such as Generative AI/Agentic AI tools (e.g. Agentic Toolchain, frontier models) to support everyday work.

Requirements

5+ years of relevant work experience with a bachelor’s degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience.

Nice To Haves

Experience in developing advanced data science models and applying machine learning techniques to business problems.
Experience in managing data science projects from scoping to delivery.
Experience in extracting and aggregating data from large data sets using SQL or other tools.
Experience in creating reproducible analytic pipelines.
Experience in data visualization using Tableau, Power BI, or R/Python.
Experience in working with modern distributed systems, including Hadoop, Hive/SQL, and Apache Spark.
Experience in collaborating with cross-functional teams to deliver scalable solutions.
Experience in communicating complex technical concepts to business audiences.
Experience in leading project teams and providing technical direction.
Extensive experience working in the data science, data engineering, or analytics profession.
Hands-on experience working with extremely large data sets and building scalable models.
Expertise in multiple programming languages (e.g., Python, R, Spark).
Experience working in global organizations and collaborating with stakeholders across geographies.
Previous exposure to financial services or payments industry is a plus but not mandatory.

Responsibilities

Deliver small to large-scale data engineering initiatives independently or as part of a cross-functional project team.
Design, develop, and maintain scalable data models, schema designs, semantic layers, and certified datasets for efficient storage, retrieval, reporting, and analytics.
Develop and implement advanced data science models, including deep learning, machine learning, recommendation systems, and generative models.
Create visualizations to communicate complex data and insights in a clear and effective manner.
Lead and provide direction to technical data science teams, ensuring high-quality and rigorous project outcomes.
Implement data pipelines using modern data engineering frameworks and platforms such as Spark, Python, SQL, Airflow, dbt, cloud-native data services, and distributed processing technologies.
Work with large-scale datasets using technologies such as Hadoop/Hive, Spark, Presto/Trino, Python, SQL, cloud data warehouses, and distributed query engines.
Collaborate with cross-functional teams (product, engineering, marketing) to gather requirements and deliver scalable solutions.
Support modern cloud-based data platforms across AWS, Azure, or Google Cloud Platform, including cloud data storage, compute, orchestration, monitoring, and security patterns.
Translate analytics output into actionable business recommendations and communicate results to senior business audiences.
Manage end-to-end data science projects, including planning, organizing, and delivering results with diverse teams.
Work with real-world, large-scale data sets to build and deploy scalable models.
Utilize modern distributed systems and data science tools (e.g., Hadoop, Hive/SQL, Apache Spark, TensorFlow, PyTorch, scikit-learn).
Ensure reproducibility and quality in analytic pipelines and project deliverables.