Sr Data Scientist

BECU•Tukwila, SC

1d•Hybrid

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. As a Senior Data Scientist at BECU, you’ll play a critical role in shaping how data drives strategic decision-making across the organization. You’ll design advanced predictive models and machine learning solutions that uncover insights, improve member experiences, and support data-informed business strategies. By partnering with cross-functional teams, you’ll translate complex business challenges into scalable analytics solutions that generate measurable impact. Your expertise will help elevate BECU’s data science capabilities, enabling more intelligent experimentation, automation, and predictive insights. Through innovation and collaboration, you’ll help turn data into meaningful outcomes that strengthen how we serve our members. 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 quantitative field, or an equivalent combination of education and professional experience.
Minimum 5 years of experience in data science or a related analytical discipline, with demonstrated success developing predictive models and delivering data-driven solutions that influence business decisions with proficiency in programming languages such as Python, R, and SQL, including experience building scalable analytical models and working with large datasets.
Working knowledge of statistical modeling, machine learning techniques, and core data engineering principles, with the ability to apply them to complex real-world problems.
Proven communication, presentation, and project management skills, with the ability to translate complex analytical findings into clear, actionable insights for technical and non-technical stakeholders.

Nice To Haves

Advanced degree (Master’s or PhD) in a quantitative discipline preferred.
Experience working within financial services or regulated industries.
Experience with cloud platforms such as AWS, Azure, or GCP.
Experience with MLOps tools such as MLflow, Airflow, Kubeflow, or similar frameworks.
Familiarity with BI tools such as Tableau or Power BI and marketing analytics platforms such as Salesforce Marketing Cloud or Google Analytics.
Experience designing systems for large-scale data processing (e.g., Snowflake, BigQuery, Redshift, Spark)

Responsibilities

Build Advanced Machine Learning Models: Design, develop, test, and deploy advanced statistical and machine learning models that solve complex business problems and deliver measurable business value.
Explore and Prepare Data for Modeling: Conduct exploratory data analysis, feature engineering, and dataset preparation to support robust model development and experimentation.
Evaluate and Improve Model Performance: Monitor and refine models to ensure accuracy, scalability, and sustained business impact while identifying opportunities for optimization.
Design and Execute Experiments: Lead hypothesis-driven experimentation, including A/B testing and controlled experiments, to inform product, marketing, and business decisions.
Contribute to MLOps and Model Deployment: Support the development and implementation of scalable MLOps frameworks to enable reliable model deployment, monitoring, and lifecycle management.
Translate Business Needs into Data Solutions: Partner with business stakeholders to understand objectives and convert complex challenges into actionable analytical strategies.
Communicate Insights Clearly: Present findings and recommendations through compelling storytelling, visualizations, and presentations tailored to both technical and non-technical audiences.
Collaborate Across Teams: Work closely with data engineers, analysts, product teams, and business leaders to integrate data science solutions into production systems and decision workflows.
Mentor and Guide Team Members: Provide guidance to fellow data scientists and analysts on technical approaches, modeling strategies, and career development.
Advance Data Science Best Practices: Contribute to the evolution of data science standards, processes, and best practices across the organization.