Lead Data Scientist

Central Pacific Bank•Honolulu, HI

About The Position

Responsibilities include performing statistical analysis, data mining, and retrieval processes on internal and external data to identify trends and other relevant information to solve or address a variety of business problems, optimize business performance, and gather business intelligence. As a senior member of the analytics organization, this role serves as both a high-level technical expert and a people leader, with responsibility for evolving and consolidating the Bank’s analytics and business intelligence capabilities. The Lead Data Scientist will lead advanced analytical initiatives—particularly those supporting credit, portfolio, fraud, collections, and broader risk management—and may directly manage a team of data scientists, credit risk analysts, and/or business intelligence analysts. The role is a key contributor to the Bank’s enterprise analytics consolidation strategy, stewarding unified data standards, analytical frameworks, BI reporting practices, and governance across the Bank. This position builds predictive models, advances credit and risk intelligence, and ensures analytics support sound decision making and compliance with regulatory expectations. Performs all duties and interacts with internal and external customers in a manner that is expressly aligned with the Company's Core Values of approaching all actions with a “Voyaging Spirit” and being “Positively Ohana”. Exhibits core competencies that result in consistent delivery of positive Customer Interactions, Empowerment and Ownership and demonstrates key professional and performance skills such as Active Listening, effective Oral and Written Communication, Action and Solution Oriented and Thoroughness.

Requirements

High School Diploma or GED equivalency required.
Bachelor’s Degree from an accredited 4-year university in advanced financial engineering, statistics, applied mathematics, econometrics, data science, or quantitative discipline required.
7+ years of experience in financial engineering, statistics, applied mathematics, econometrics, data science, or quantitative discipline required.
4+ years of Management experience, including people required.

Nice To Haves

Master’s Degree or PhD in quantitative discipline preferred.
1+ years of banking industry experience preferred.

Responsibilities

Performing Analytics: Responsible for completing analytical work using a range of tools and methodologies some of which might be proprietary to the organization including: Draw conclusions and effectively influence business strategies.
Perform statistical analysis to understand and work around possible limitations in models.
Develop hypotheses and test them with careful experiments.
Provide support as required for data-driven decision-making.
Lead: Designs, develops, and deploys predictive models, statistical analyses, risk scorecards, and machine learning solutions to support credit and risk strategies (e.g., RAROC, pricing, loss forecasting, early warning, portfolio monitoring). Also performs model validations or model validation assessments.
Performs complex data mining using large internal and external datasets, identifying trends and opportunities to improve portfolio performance and operational efficiency.
Leads and manages a team of BI analysts, and/or credit analysts—including prioritization, development, mentoring, code reviews, performance management, and technical coaching.
Conducts hypothesis testing and experimental design to validate risk strategies and proposed business solutions.
Ensures analytical outputs are accurate, repeatable, and aligned with business needs; translates results into actionable insights for senior leaders and risk partners.
Partners with Enterprise Risk, Credit Administration, and key product areas to support model monitoring, governance, documentation, and regulatory expectations.
Managing/Leading Stakeholder Communications Develop communication plans to actively engage key stakeholders groups.
Partner with business sponsors to determine needs and wants.
Lead: Serves as the subject matter expert (SME) for predictive analytics, credit risk modeling, BI dashboards, and data driven insights.
Translates complex technical concepts into clear, business aligned recommendations for executives and functional leaders.
Develops communication and engagement plans for project sponsors, risk committees, auditors, and cross-functional partners.
Facilitates collaborative solutioning across credit, risk, operations, finance, compliance, and technology organizations.
Data Quality, Security, Reporting, & Governance Support efforts to source, scrub, and join data from public, commercial & proprietary sources.
Develop approach to data quality to increase the bar on the soundness of our data.
Ensure compliance with bank policies for how data is used and manipulated in the business.
Identify data integrity issues and work with Data Quality Validation unit to correct issues.
Lead: Oversees team practices for sourcing, validating, cleansing, and joining data from numerous internal and external systems.
Troubleshoots data quality issues, partnering with Enterprise Data to remediate root causes.
Ensures BI dashboards and credit/risk reporting adhere to consistent standards, controls, and governance practices.
Maintains compliance with data security policies, model governance, and regulatory requirements (including credit and consumer regulatory expectations).
Enterprise Analytics & BI Consolidation Strategy Leads the Bank’s analytics consolidation efforts by defining unified analytic standards, solutions, processes, and data environments across credit, risk, operations, and consumer/commercial lines of business.
Evaluates the existing data and BI landscape, identifying opportunities to centralize tools, rationalize reports, eliminate redundant processes, and streamline data pipelines.
Defines the technical architecture for advanced analytics and business intelligence, recommending modern platforms, tools, and automation approaches.
Identifies and integrates diverse data sources—including credit bureau data, economic indicators, operational data, and risk datasets—to strengthen predictive and monitoring capabilities.
Partners closely with Technology, Enterprise Data, and Information Security to operationalize analytics, improve data governance, and ensure sustainable, compliant workflows.
Monitors emerging analytics technologies, regulatory trends, and modeling best practices to drive innovation in credit and risk analytics.