Interested in this Data Scientist role at BECU?
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About This Role
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.PAY RANGE
The Target Pay Range for this position is $117,200\.00\-$143,200\.00 annually. The full Pay Range is $91,000\.00 \- $169,400\.00 annually. At BECU, compensation decisions are determined using factors such as relevant job\-related skills, experience, and education or training. Should an offer for employment be made, we will consider individual qualifications. In addition to your salary, compensation incentives are available for the hired applicant. Incentives are performance based and targets vary by role.BENEFITS – because *people helping people* starts with supporting *you 401(k) Company Match (up to 3%)**
- 4% annual contribution to your 401(k) by BECU
- Medical, Dental and Vision (family contributions as well)
- PTO Program \+ Exchange Program
- Tuition Reimbursement Program
- BECU Cares volunteer time off \+ donation match
To join our dynamic team, we require candidates to be residents of WA,CA, VA, NC,OR, ID, AZ, TX, GA, or SC. If you’re located in Washington state and within a reasonable driving distance from Tukwila, we are requesting that you come into our HQ on Tuesdays \& Wednesdays. For those candidates that live outside the commute distance of TFC and in any of our approved remote work locations, this role will be remote. Remote or onsite, we are committed to ensuring you are fully engaged and included in our collaborative environment.IMPACT YOU’LL MAKE
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.WHAT YOU’LL DO* 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 toMLOpsand 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.
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.WHAT YOU’LL GAIN* The opportunity to solve complex, high\-impact problems using advanced analytics and machine learning.
- A chance to influence enterprise decision\-making through predictive modeling, experimentation, and data storytelling.
- Hands\-on experience building and scaling models within modern cloud data environments and MLOps frameworks.
- Collaboration with cross\-functional leaders across product, marketing, analytics, and engineering teams.
- Opportunities to mentor peers and shape the evolution of data science practices at BECU.
- The ability to make a direct impact on member experience, financial wellbeing, and organizational strategy.
QUALIFICATIONS*Minimum Qualifications** 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.
*Desired Qualifications** 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)
JOIN THE JOURNEY
Ready to make an indelible impact? Eager to be part of a collaborative and innovative team where your ideas and contributions don’t just fill a role but fuel the growth and success of BECU? This is more than a job – it’s a chance to elevate your career, skills, and future, all while contributing to the robust operational landscape of BECU.
Embrace the opportunity to grow with us. Apply now, bring your expertise to the table, and let’s achieve excellence together at BECU. Your journey of influence, innovation, and impactful contribution starts now. \#BECU \#YourGrowth \#BECUJourneyEEO Statement:
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BECU is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, veteran status, disability, sexual orientation, gender identity, or any other protected status.
About Us
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From our very beginning, BECU has been about people helping people. As a financial services cooperative, our purpose has and always will be the same: we work together to best serve our members and improve the financial well\-being of our community. With 90 years and over one million members later, we're still rooted in our values, practices and mission \- and even more passionate about our future.
As one of the nation's leading credit unions, we're not driven by profit. We're owned by our members and their interests are at the core of everything we do. Now we're growing faster than ever before\-but we'll never forget our roots. To continue doing right by our members, we believe we must first do right by our people. Here, you'll receive the resources and support you need to learn, grow and build a meaningful career. Because we know it's our people who make us special.
Salary Context
This $117K-$169K range is below the median for Data Scientist roles in our dataset (median: $166K across 345 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At BECU, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $204,700 based on 441 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($143K) sits 30% below the category median. Disclosed range: $117K to $169K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
BECU AI Hiring
BECU has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Tukwila, WA, US. Compensation range: $169K - $186K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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