Sr Data Scientist - Member Growth & Prescreen Modeling

Boeing Employees' Credit Union•Tukwila, WA

3d•Hybrid

About The Position

This role is for a Senior Data Scientist focused on Member Growth & Prescreen Modeling at BECU, a financial institution with 1.5 million members and over $30 billion in managed assets. BECU operates on a "people helping people" philosophy, emphasizing service to members, communities, and employees. The company is undergoing business and technology transformation, making it an exciting time to join. The role involves shaping how data drives member growth, marketing strategy, and enterprise decision-making by partnering with Marketing, Product, and Technology teams to solve complex business challenges using predictive analytics, machine learning, and experimentation. The goal is to improve member acquisition, cross-sell opportunities, and campaign performance by turning complex data into practical, scalable solutions and strengthening personalized member experiences.

Requirements

Bachelor's degree with emphasis in Data Science, Computer Science, Statistics, Mathematics, Economics, or a related quantitative discipline, or equivalent work or educational experience required.
Minimum 5 years of experience developing predictive analytics and machine learning solutions, with demonstrated success designing statistical models, applying advanced analytical techniques, and translating business problems into scalable, data-driven solutions using Python, SQL, R, or similar programming languages.
Demonstrated ability to communicate complex analytical concepts to technical and business audiences while managing multiple initiatives through strong analytical, problem-solving, organizational, and critical thinking skills.
Candidates to be residents of WA, CA, VA, NC, OR, ID, AZ, TX, GA, or SC.

Nice To Haves

Advanced degree preferred.
Experience working within financial services or another highly regulated industry preferred.
Experience with cloud platforms such as AWS, Azure, or GCP preferred.
Experience with MLOps tools such as MLflow, Airflow, Kubeflow, or similar frameworks preferred.
Experience supporting customer acquisition, direct marketing, CRM analytics, prescreen marketing, campaign optimization, or customer lifetime value modeling preferred.
Experience with large-scale data platforms such as Snowflake, Spark, BigQuery, or Redshift preferred.

Responsibilities

Collaborate with Marketing, Product, and business stakeholders to understand member acquisition, prescreen, prospecting, and cross-sell objectives, translating business needs into analytical strategies that improve targeting, eligibility, and campaign performance.
Design, develop, validate, and maintain statistical and machine learning models that support prescreen targeting, prospect prioritization, propensity modeling, segmentation, eligibility decisioning, and uplift modeling.
Lead A/B testing, champion-challenger testing, and experimentation efforts to evaluate campaign effectiveness, identify performance drivers, and improve member engagement, conversion, and marketing ROI.
Analyze complex datasets and communicate actionable recommendations that quantify business impact through metrics such as conversion lift, campaign effectiveness, engagement improvement, operational efficiency, and return on investment.
Partner with Data Engineering and Technology teams to deploy scalable machine learning models, support production-ready analytical solutions, and maintain reliable data pipelines that enable continuous optimization.
Present analytical findings, recommendations, and strategic tradeoffs through compelling visualizations and executive-ready presentations that influence business decisions across technical and non-technical audiences.
Work closely with data engineers, analysts, product managers, marketers, and business leaders to integrate predictive analytics into enterprise decision-making and operational workflows.
Provide guidance, coaching, and technical mentorship to fellow data scientists and analysts while promoting best practices across the data science community.
Contribute to the evolution of enterprise data science standards, modeling approaches, experimentation frameworks, and MLOps capabilities to improve scalability and long-term success.
Perform other duties as assigned to support the evolving needs of the team and organization.