Sr. Associate, Data Science

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Apache Hadoop, Scala Programming, Machine learning techniques, Data science techniques, PyTorch, TensorFlow, MapReduce, R Programming

Job Description

Sr. Associate, Data Science

Boston, United States of America

WHAT YOU WILL BE DOING

The Sr. Associate, Data Science 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.

Essential Functions/Responsibility Statements:

• 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.

Qualifications:

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.

Education:

• Bachelor's Degree or equivalent work experience

Work Experience:

• 9+ years ; Data mining/advanced analytics applied to large-scale data-intensive projects.

Skills and Abilities:

• 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 algorithims, 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.
• Ability to conceptualize and execute analytical work based on broad business objectives.
• Ability to work with large scale data to derive insights through data analysis, optimization or risk classification algorithms.
• Ability to quickly learn new tools and understand new areas of data.

At Santander, we value and respect differences in our workforce and strive to increase the diversity of our teams. We actively encourage everyone to apply.

Santander is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, genetics, disability, age, veteran status or any other characteristic protected by law.