Senior Data Scientist & Lead Production Engineer

WaFd is building a high-production, code-first, data science group to make WaFd's data serve WaFd's people. The team's focus is to create and productionize data science code that continuously delivers the information the business personnel need to be the best service providers in banking. This position is responsible for the development and daily delivery of production-ready data science code, as well as code management and deployment.
We are looking for an individual who loves automation and is passionate about data, solving problems, and serving people; has a background in statistics, banking, finance, software development, code deployment and management; has a vision for data science infrastructure and the continuous improvement of our data science product life cycle; is fun to work with, curious, and creative; who is driven to make a lasting impact, and who support our basic team principles and vision for data science. We want people who want to move the world forward. WaFd Data Science Principles: Serve People; Seek Simplicity; Create Long-term Value WaFd Data Science Vision: Empower employees and clients by getting valuable information to their fingertips. Position Summary
This position is a code-first position. The emphasis is on delivering daily, well-written code to develop, automate, and maintain our data science products. In addition, the position oversees the technical vision and direction of WaFd's data science production infrastructure and architecture, specifically designing, building, and configuring the internal systems, pipelines, packages, etc. that WaFd needs to efficiently deliver data science to our team members and grow as a service organization. Primary Responsibilities

• Code Authorship: write generalized, reusable, simple, well-documented code for data science and data science infrastructure, specifically:
  • Construct dashboards and information delivery systems
  • Generalize and re-engineer existing code for efficiency, speed, and edge cases
  • Integrate bank systems by extracting, cleaning, transforming, and pushing data
  • Construct and implement models as business needs arise, such as explaining the impact of particular factors, predicting client behavior or financial outcomes, etc.
  • Collect, clean, validate, transform, and place data
  • Other, as needed by the business
• Code Management: develop automated processes for code testing, implementation, productionization, and reproducibility, specifically:
  • unit and regression testing
  • monitoring and ensuring product up-time
  • maintaining, updating, or re-engineering ontology for code base
• Design, build, automate, and configure the architecture, pipelines, package management, etc. that improve the efficiency of our code development
• Analyze and mitigate performance issues and scalability constraints
• Documentation of code, processes, and systems (as simple and pithy as possible to communicate important info)
• Provide mentorship and direction to new data scientists

Requirements: Education and Training
Master's degree or Ph.D in computer science, mathematics, engineering, physics, quantitative finance, or other technical discipline, or equivalent work experience. Previous Experience
Ten years of experience: - developing and implementing data science and data science infrastructure - writing in multiple programming languages, frameworks, and platforms - creating and using microservices, APIs, ESBs, and containers - performing rapid development and deployment via Agile practices Knowledge/Skills

• Git, GitHub, Repos, Package management,
• Linux, server configuration, AWS, Azure Devops
• Clusters and distributed compute (Kubernetes), GPU compute
• Regression and unit testing, production error catching and reporting
• Automated UI testing, load, and performance testing
• Modeling algorithms (OLS, logistic and other wide-margin separation, non-parametrics, bayes, simulation MCMC, clustering, ensembles, evolutionary algorithms, etc.)
• Encryption standards (SSH, etc)
• API construction, database and system connections, etc.
• Documentation (markdown, LaTeX, )
• Languages: Go, R, Python, Rust, C++, JS, or the languages of your choice with a strong bias toward open-source