DESCRIPTION:

Duties: Lead the design, development, and implementation of machine learning models to drive impactful decisions throughout the customer lifecycle, including acquisition, account management, credit authorization, and collections. Collaborate with cross-functional teams, including Marketing, Risk, Technology, and Model Governance to deliver innovative modeling solutions and ensure successful production deployment. Anticipate and promptly address stakeholder needs, ensuring alignment and satisfaction. Present model results and insights to senior leaders, effectively communicating insights and recommendations. Ensure compliance with internal and external model governance and regulatory requirements, design model explainability methods for risk estimation models. Lead and mentor team members, fostering an inclusive environment that promotes continuous learning and growth. Utilize technical proficiency in SAS, Python, or equivalent programming languages for data analysis, modeling tasks, and the development of credit risk scorecards. Perform data cleaning, feature engineering, selection, and hyper-parameter tuning for statistical and advanced machine learning techniques.

QUALIFICATIONS:

Minimum education and experience required: Master's degree in Applied Statistics, Information Science, Computer Science, Computer Engineering, Information Technology, Statistics, Data Analytics or related quantitative field of study plus 5 years of experience in the job offered or as Applied AI/ML Lead, Machine Learning Engineer, Structural Analyst, or related occupation.

Skills Required: This position requires three (3) years of experience with the following: designing, implementing, and managing machine learning pipelines on distributed computation platforms such as Apache Spark, Hadoop, or HDFS for large-scale data processing; programming with Python focusing on data manipulation, analysis, and machine learning libraries including Pandas, NumPy, Scikit-learn, and PyTorch; developing and applying statistical models and advanced machine learning techniques including XGBoost, GLM, Random Forest, Neural Networks, Clustering, and K-Nearest Neighbors (KNN); interpreting and communicating the results of machine learning models to both technical and non- technical stakeholders. This position requires any amount of experience with the following: cloud-based solutions for scalable machine learning model development and management; data warehouse solutions such as Teradata, Snowflake, or Hive for data storage and retrieval; JVM-based programming languages, including Java and Scala; applying model explainability methods to produce global and local explainability for machine learning models; collecting, organizing, interpreting, and summarizing numerical data to extract actionable insights and identify patterns or anomalies.

Job Location: 201 N Walnut St, Wilmington, DE 19801.