Data Scientist [Multiple Positions Available]

DESCRIPTION:

Duties: Drive the design, build, and implementation of end-to-end data products that systemically publish actionable insights to commercial bankers and their clients.

Create product recommendation models for core banking products utilizing internal and external data to inform modeling approaches, produce prototypes, and implement into production.

Perform analytics exploring the relationship of companies, products, and banker engagement to inform the product roadmap and top-down decision-making.

Drive collaboration across information architecture, data engineering, applied science, business intelligence, product management, data architecture, software engineering, and business analytics to bring products to market and architect data-driven solutions.

Guide discovery of new data science solutions, collaborating directly with business and tech leaders across banking, product management, engineering, and applied science.

Drive data migrations from legacy banking platforms to new platforms to improve system performance and reliability.

Adopt new technologies and establish best practices to keep analytical solutions current, detect, diagnose, correct, and simplify problems.

Explain data problems and solutions to business leaders across multiple levels of the banking organization.

QUALIFICATIONS:

Minimum education and experience required: Master's degree in Business Analytics, Data Science, Statistics, or related field of study plus two (2) years of experience in the job offered or as Data Scientist, Business Analyst, Data Consultant, or related occupation.

Skills Required: This position requires two (2) years of experience in the following: Creating and maintaining models consisting of banking, financial, and external data sources; Building insights ecosystems to standardize disparate analytics into a single business-facing feed of insights; Designing and implementing scalable AI/ML models in large-scale enterprise environments using Databricks and MLflow; Developing end-to-end pipelines, managing model versioning, and enabling automated deployment and monitoring for production readiness; Defining business requirements and translating them into technical solutions; Optimizing model training and inference workflows for efficiency and scalability; Applying multivariate statistical methods including principal component analysis (PCA), linear discriminant analysis (LDA), and cluster analysis to uncover data patterns, reduce dimensionality, and inform feature engineering; Performing data preprocessing, outlier detection, and variable selection; Conducting exploratory data analysis to identify relationships and trends in high- dimensional datasets; Implementing feature selection and transformation techniques to improve model performance; Developing quantitative models to extract insights from enterprise datasets, including time series analysis, predictive modeling, and risk assessment; Validating model assumptions and assessing statistica...