Data Science VP

Data Science VP

Country: United States of America

Your Journey Starts Here:

Santander is a global leader and innovator in the financial services industry. We believe that our employees are our greatest asset. Our focus is on fostering an enriching journey that empowers you to explore diverse career opportunities while nurturing your personal growth. We are committed to creating an environment where continuous learning and development are prioritized, enabling you to thrive both professionally and personally. Here, you will find ample opportunities to connect and collaborate with talented colleagues from around the world, sharing insights and driving innovation together. Join us at Santander, where you are supported by a culture of engagement and a commitment to your success.

An exciting journey awaits, if you are interested in exploring the possibilities We Want to Talk to You!

The Difference You Make:
The Data Science VP is primarily responsible for leading and/or overseeing analytic projects to support key business strategies and initiatives, communicating conclusions and results to senior management. They will design, deploy, and enhance predictive or behavioral models to address critical business issues, improve automation and overall efficiencies.

• Conducts data exploration, hypothesis creation, algorithm testing, and scaling to large data-sets.

• Designs, deploys, and enhances predictive or behavioral models to address critical business issues, improve automation and overall efficiencies.

• Provides analytics support across lifecycle initiatives: originations, line management, portfolio/customer risk management, capital risk management, and collections & recovery.

• Leads analytic projects to support key business strategies and initiatives, communicates conclusions and results to senior management.

• Performs large-scale data mining and data science projects as well as ad hoc statistical and data mining analyses.

• Presents results of analyses to business units, explain technical and statistical concepts to non-technical/non-statistical associates and vice-versa.

• Leverage subject matter expertise and analytical skills to drive sustainable and profitable growth strategies across the business ensuring compliance with all policies and Local, State, and Federal laws and regulations.

• Develops dashboards and related reports to highlight key performance metrics and trends.

• Engages the right technical and/or business partners, and collaborates with a wide range of teams to readily share results and lessons learned.

• Researches and develops predictive analytics for continuous optimization of business processes using problem sets based on structured and unstructured data.

• Partners with business teams to gather and process data, provides feedback on patterns and insights based on detailed data analysis.

What You Bring:
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

• Bachelor's Degree or equivalent work experience: Data Science, Data Analysis, Operational Research, Decision Management or equivalent quantitative field. - Required.

• Master's Degree or equivalent work experience: Data Science, Data Analysis, Operational Research, Decision Management or equivalent quantitative field. - Required.

• Ph.D.: Data Science, Data Analysis, Operational Research, Decision Management or equivalent quantitative field. - Preferred.

• 10+ Years Data mining/advanced analytics applied to large-scale data-intensive projects. - Required.

• 10+ Years experience with SPG products such as RMBS and CMBS - Required.

• Advanced knowledge of and breadth of analytic methodologies - statistics & machine learning - e.g. linear and non-linear regression, logistic regression, clustering (k-means, EM, Mean-shift), classification algorithms, decision trees, ensembles (bagging, boosting, rand).

• Demonstrated experience with any and all of the following: credit decision engine packages, statistical techniques, statistics software packages, small business/commercial risk assessment, retail and wholesale capital calculations, and data mining techniques.

• Strong knowledge of distributed computing, data warehouse, data mining, business analytics and software development.

• Strong analytics background with attention to details to deliver quality results.

• Strong hand