Due to the on-site requirements of this role, preference will be given to candidates currently residing in Colorado. This position is not eligible for full-time remote work. Relocation assistance is not provided.

Ent Credit Union and Wings Credit Union joined forces in January 2026. This merger means more opportunities, expanded resources, and a shared commitment to delivering exceptional member service. Together, we become more - empowering members, communities, and teams through a bold, unified future. Both organizations bring a strong legacy of member satisfaction, operational excellence, financial stability, and community impact. Recognized locally and nationally as best-in-class financial institutions and employers of choice, each is known for its commitment to financial well-being and philanthropic leadership. Join us during this transformative time and be part of shaping the future of banking! To learn more about the merger, click here.

Job Description

Wings is seeking a curious, motivated, and innovative Entry-Level Machine Learning Engineer to join our growing team. This is an exciting opportunity for someone looking to launch their career in machine learning while contributing to meaningful, data-driven initiatives that support business growth and member experiences.

In this role, you will partner with cross-functional teams to understand business objectives and develop machine learning solutions that transform data into actionable insights. You will analyze complex datasets, build and monitor predictive models, create visualizations, and support the implementation of scalable machine learning and analytics solutions. Along the way, you'll gain hands-on experience, learn from experienced professionals, and collaborate with teams across the organization to solve real-world business challenges.

The ideal candidate has a solid foundation in machine learning and data science concepts, strong analytical and problem-solving skills, and a passion for continuous learning. Success in this role requires curiosity, adaptability, collaboration, and a desire to leverage data and technology to drive innovation and business results.

Essential Functions

Perform data preprocessing, feature engineering, and data cleansing to prepare datasets for analysis and model construction.
Collaborate on the development and implementation of machine learning models and analyses to deliver member/business value.
Assists in building and prototyping dashboards to monitor model performance.
Explore, identify and document necessary data for projects and understand their underlying structures.
Assist in monitoring, updating, and testing Machine Learning pipelines.
Bank Secrecy Act: Remains cognizant of and adheres to Wings policies and procedures, and regulations pertaining to the Bank Secrecy Act.

Qualifications

Minimum Formal Qualifications for this Position:

Bachelor's Degree with an emphasis on coursework of a quantitative nature i.e. Data Science, Machine Learning, Statistics, Information Systems, or a related area, required.
3+ years' experience in analytics, business intelligence, data engineering, or data science using modern analytics and data management technology, required.

Technical or Specialized Knowledge/Skills:

Demonstrated experience in applied machine learning, utilizing common libraries like scikit learn, PyTorch, or TensorFlow to develop and assess complex models.
Proficient in Python & SQL for handling and manipulating large datasets.
Experience with business intelligence and data visualization tools, preferably Tableau.
Ability to effectively manage competing priorities in an ambiguous environment.
Familiarity with cloud-based data platforms such as Databricks, Azure, AWS, etc..
Verbal and written communication skills, capable of conveying rigorous mathematical concepts to non-experts effectively.
Ability to critically think, troubleshoot, and demonstrate proactive problem-solving skills.
Organizational skills and attention to detail.

Additional Information

PAY RANGE: $93,000 to $109,000 Annually (I15), plus 10% annual target bonus.

Final compensation for this position will be determined by various factors such as relevant work experience, specific skills and competencies, education, certifications, location and internal pay equity.