Sr. Data Scientist

Visa•Washington, DC

61d•$145,300 - $232,700•Hybrid

About The Position

Visa U.S.A. Inc., a Visa Inc. company, needs a Sr. Data Scientist (multiple openings) in Washington, DC to use and build predictive models to innovate and optimize customer experiences, revenue generation, data insights, advertising targeting, and other business outcomes. This role involves managing client and stakeholder relationships, particularly the technical components and their communication to non-technical members. The position provides technical leadership in a team that generates business insights based on big data, identifies impactful recommendations, and communicates findings to clients. It requires applying data science techniques and innovative thinking to unique datasets to help clients solve complex problems and drive innovation. The role also involves designing, scheduling, and orchestrating complex data workflows and ETL pipelines using Apache Airflow, applying statistical hypothesis testing methods, and developing interactive data visualizations and dashboards. The Sr. Data Scientist will lead data science projects from scoping to delivery, connect with clients to understand their challenges and deliver solutions, identify opportunities for scalable products, and collaborate across the organization. Mentorship in modern analytic techniques and their business applications is also expected, along with prioritizing and managing multiple data science projects involving diverse, cross-functional teams. Domestic travel is required up to 5% of the time. The position reports to the Washington, DC office and may allow for partial telecommuting.

Requirements

Master's degree in Data Science, Engineering or related field and 2 years of experience in the job offered or in a Data Scientist-related occupation.
Analyzing and working with large data sets using programming languages such as Python, R, SQL, Hive, and Spark.
Data access tools including Presto, DBViz, DataGrip, and MLP.
Developing and optimizing machine learning models and algorithms using scikit-learn, xgboost, Pytorch, TensorFlow, Keras, Optuna and ARIMA.
Data automation and MLOps experience (Tuber, Airflow and MLFlow).
Presenting evidence-based insights and conveying relevant recommendations to stakeholders.
Data visualizations and dashboarding tools such as Plotly, Tableau, and Power BI to communicate insights effectively to technical and non-technical stakeholders.
Experiment design and statistical hypothesis testing methods including Bayesian methods, t-tests, chi-square tests, ANOVA, non-parametric tests, and A/B testing to evaluate business strategies.
Distributed computing experience using Ray.
Communication with non-technical audiences and ability to develop and drive a presentation.
Knowledge of familiarity implementing advanced statistical approaches to new and existing pipelines.
Building custom python packages for automation.
Experience navigating the payments landscape in a project management capacity, with a strong understanding of industry dynamics.

Responsibilities

Use and build predictive models to innovate and optimize customer experiences, revenue generation, data insights, advertising targeting, and other business outcomes.
Manage client and stakeholder relationships – particularly the technical components and the communications of those technical components to non-technical members.
Provide technical leadership in a team that generates business insights based on big data, identifies impactful recommendations, and communicates findings to clients.
Apply data science techniques and innovative thinking to our unique datasets to help clients solve complex problems and drive innovation.
Utilize tools such as Presto, DBViz, DataGrip, and MLP for data access, and modeling frameworks like Pytorch, Gradient boosted trees (ex xgboost, catboost, and lightgbm) and time series forecasting for model development.
Designing, scheduling, and orchestrating complex data workflows and ETL pipelines using Apache Airflow to ensure reliable and scalable data processing.
Applying statistical hypothesis testing methods (t-tests, chi-square tests, ANOVA, non-parametric tests) to evaluate business hypotheses and guide strategic recommendations.
Developing interactive data visualizations and dashboards using tools such as Plotly, Tableau, and Power BI to communicate insights effectively to technical and non-technical stakeholders.
Lead data science projects from scoping to delivery, engaging with internal and external partners throughout the process.
Connect with clients to understand their challenges and use data to deliver compelling, actionable solutions.
Identify opportunities to develop scalable products from analyses that can benefit multiple clients.
Collaborate across the organization to uncover opportunities where Visa data can drive business solutions.
Synthesize ideas and proposals in writing and engage in productive discussions with internal and external stakeholders.
Provide mentorship in modern analytic techniques and their business applications.
Prioritize and manage multiple data science projects involving diverse, cross-functional teams.