Senior Python Engineer Jobs in Olympia, WA (NOW HIRING)

Education

• Bachelor's degree in a quantitative field required.

• Master's or PhD in a quantitative discipline preferred.

Experience

• 6+ years of professional experience or equivalent relevant work.

• Proven track record leading endtoend data science projects with measurable business impact.

Technical Expertise

Core Data Science Capabilities (expert in at least two, strong in others):

• Programming & Automation:

  • Python required; experience with automation, DevOps practices, APIs, file I/O, and database integrations.

  • Experience engineering solutions in cloud environments (AWS preferred; Azure/Google comparable).

  • Exposure to objectoriented development and scalable architecture.

• Data Visualization:

  • Expertise across multiple visualization tools and techniques.

  • Ability to tailor visuals to business use cases and audiences.

• Statistics & Machine Learning:

  • Deep knowledge of statistical inference, regression, feature selection, feature extraction, and ML algorithms.

  • Experience leading large-scale modeling projects end-to-end.

  • Familiarity with generative AI approaches is a plus.

• Data Engineering / ETL:

  • Strong SQL skills; ability to design, debug, and optimize complex queries.

  • Ability to navigate and explore large databases independently.

  • Experience combining internal and external data sources.

Soft Skills & Business Leadership

• Strong communication skills, including the ability to influence senior leaders.

• Project management expertise and strong business acumen (financial services experience a plus).

• Ability to manage multiple concurrent initiatives in a fastmoving environment.

• Comfortable leading engagements and representing analytics with executive leadership.

Primary Responsibilities

Analytical Solution Development

• Design, develop, and execute analytical solutions using optimization, simulation, machine learning, generative AI, and statistical modeling.

• Construct predictive models to explain events, forecast behaviors, identify risk, or perform segmentation and clustering.

• Apply domain expertise to ensure models are practical, interpretable, and aligned with business needs.

• Evaluate alternative approaches and select appropriate modeling techniques for each use case.

Data Engineering & Preparation

• Integrate and transform large volumes of data from diverse sources (e.g., DB2, SQL Server, Teradata, APIs) to support analytics and experimentation.

• Build modelingready datasets using validation, reconciliation, feature engineering, and aggregation techniques.

• Write complex SQL queries involving multitable joins, data exploration, and troubleshooting with minimal guidance.

• Develop logical data models combining internal and external datasets; lead conversations with external data providers when needed.

Automation & Deployment

• Build automated analytics pipelines leveraging scripting, APIs, DevOps practices, and cloud platforms.

• Partner with engineering and IT teams to scale solutions, automate workflows, and integrate models into business processes.

• Play a lead role in operationalizing AI/ML solutions within production environments.

Visualization, Insights & Communication

• Develop and deliver clear, compelling visualizations (static or dynamic) tailored to various audiences.

• Interpret analytical results and communicate actionable insights that influence senior leaders and key business partners.

• Translate complex technical work into businessfriendly recommendations.

Leadership, Mentorship & Collaboration

• Coach, mentor, and develop junior data scientists; provide technical guidance and feedback.

• Provide leadership on data science initiatives, ensuring outputs meet quality standards.

• Work in a collaborative, innovation-focused environment with product owners, engineers, data architects, and business partners.

• Manage multiple projects simultaneously, prioritizing independently and guiding less experienced team members.

Innovation & Research

• Stay current on emerging statistical methods, machine learning advancements, and generative AI tools.

• Conduct independent R&D to prototype new approaches and explore innovative solutions for highvisibility business problems.

• Demonstrate entrepreneurial, selfstarter mindset with a strong curiosity and continuouslearning orientation.