Data Scientist

BECU•Tukwila, SC

9d•Remote

About The Position

Is it surprising to hear that a financial institution of 1.5 million members and over $30 billion in managed assets say that success comes from focusing on people, not profits? Our “people helping people” philosophy has guided us since 1935, driving our deep commitment to serving our members, communities, and each other. When you join our team, you become part of a purpose-driven organization where your work makes a real difference. While we’re proud of our history, we’re even more excited about our future. With business and technology transformation on the horizon, there’s never been a better time to be part of BECU. IMPACT YOU’LL MAKE As a Data Scientist at BECU, you’ll help shape how we understand and engage with our members by turning complex data into meaningful insights and predictive solutions. You’ll build models that identify opportunities to strengthen member relationships, improve onboarding experiences, and support financial wellbeing. Working closely with cross-functional partners across marketing, analytics, and product teams, you’ll uncover patterns that help BECU anticipate member needs and drive smarter decisions. Your work will power experimentation, predictive insights, and data-driven strategies that elevate how we serve our members. Through advanced analytics and storytelling, you’ll help transform data into impactful member experiences. This isn’t just about ticking off tasks on a list. It's about making a significant, positive change in BECU’s journey, where your contributions are valued, and your growth is continually fostered.

Requirements

Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field, or equivalent combination of education and experience.
Minimum 2 years of experience in data science, including at least 1 year applying data science in a business or industry setting with proficiency in programming languages such as Python or R, with the ability to build, test, and deploy analytical models.
Working knowledge of statistical analysis and machine learning techniques, with the ability to apply them to real-world business problems.
Proven communication, presentation, and project management skills, with the ability to translate complex analytical findings into clear insights for diverse stakeholders.

Responsibilities

Build Predictive Models: Develop, validate, and deploy statistical and machine learning models that help identify member behaviors, such as engagement patterns, product adoption opportunities, and potential member dormancy or attrition.
Design Experiments and A/B Tests: Lead methodology design for controlled experiments to evaluate onboarding strategies, engagement programs, and marketing initiatives while ensuring statistical rigor and actionable insights.
Transform Data into Insights: Translate complex analytical findings into clear, compelling narratives using visualizations, dashboards, and presentations that support strategic decision-making.
Collaborate Across Teams: Partner with marketing, product, analytics, and engineering teams to define business problems, gather requirements, and deliver data-driven solutions that improve member experiences.
Develop Advanced Datasets: Build and maintain intermediate to complex datasets—including structured and unstructured data—using tools such as Python, SQL, or Spark to support modeling and analysis.
Explore Data for Strategic Opportunities: Use analytical research and exploratory techniques to uncover trends, opportunities, and emerging insights that support divisional and organizational goals.
Support Data Science Excellence: Participate in peer reviews, knowledge-sharing forums, and data science communities to promote high-quality analytics practices and continuous learning.
Manage Model Lifecycle: Monitor model performance, evaluate results, and optimize models over time to ensure continued accuracy, reliability, and business value.
Adapt and Support Business Needs: Take on additional responsibilities and initiatives that support evolving data science priorities across the organization.