Sr. Data Engineer - Ai/Ml

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Day - 08 Hour (United States of America)
Stanford Healthcare appointed its first Chief Data Scientist in early 2022 to advance the use of artificial intelligence in patient care at Stanford Medicine. As part of this effort, we are building a team to bring Artificial Intelligence (AI), predictive algorithms and other emerging machine learning (ML) based innovations in data science into routine use in healthcare. We are looking for a Sr Data Engineer to lead our infrastructure development efforts which involve working across multiple cloud environments and health data sources. The key stakeholders are a data science team building models in Python.
We are still early in our journey and approach so there's a lot of room to craft and improve our tech stack. This is a great opportunity to use your skills and expertise to impact clinical care.
This is a Stanford Health Care job.
A Brief Overview
The Data Engineer will be part of a team building Stanford Health Care's (SHC) solutions incorporating Artificial Intelligence including providing health care solutions in the areas of patient care, medical research and administrative services. This group is designed to bring Artificial Intelligence (AI), predictive algorithms and other emerging machine learning (ML) based innovations in data science into healthcare and will partner closely with individuals across clinical specialties and operations areas to deploy algorithms that can lead to better patient outcomes.
Reporting to the Data Science Director and working closely with Stanford Medicine's inaugural Chief Data Scientist, this role will be responsible for maintaining compute frameworks, analysis tooling, and/or model implementations used or created by the team. The individual will design, implement, and support data processing software and infrastructure.
Locations
Stanford Health Care
What You Will Do

• Understand the requirements of data processing and analysis pipelines and make appropriate technical design and interface decisions. Elucidating these requirements will require training, developing, and validating researcher-built or vendor provided machine learning algorithms on hospital data as well as working with other members of the data science team.
• Troubleshoot and debug environment and infrastructure problems found in production and non-production environments for projects by the Data Science Team.
• Work with other groups at SHC and the Technology and Digital Solutions (TDS) group to ensure servers and system maintenance based on updates, system requirements, data usage, and security requirements
• Understand data flows among the SHC applications and use this knowledge to make recommendations and design decisions for languages, tools, and platforms used in software and data projects.
• Build end-to-end data pipelines and infrastructure for ML models used by the Data Science team and others at SHC.

Education Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related, or equivalent working experience

Experience Qualifications

• Experience working with healthcare data.
• Experience building and working with data infrastructure in a SaaS environment
• 3-5 years’ experience in building data infrastructure for analytics teams, including ability to write code in SQL, R, or Python for processing large datasets in distributed cloud environments
• Experience overseeing, developing or implementing machine learning operations (MLOps) processes.
• Experience with cloud deployment strategies and CI/CD

Required Knowledge, Skills And Abilities

• Knowledge of resource management and automation approaches such as workflow runners.
• Knowledge of multiple programming languages, commitment to choosing languages based on project-specific requirements, and willingness to learn new programming languages as necessary.
• Collaborative mentality and excitement for iterative design working closely with the Data Science team.

Physical Demands and Work Conditions
Blood Borne Pathogens

• Category II - Tasks that involve NO exposure to blood, body fluids or tissues, but employment may require performing unplanned Category I tasks

These Principles Apply To ALL Employees
SHC Commitment to Providing an Exceptional Patient & Family Experience
Stanford Health Care sets a high standard for delivering value and an exceptional experience for our patients and families. Candidates for employment and existing employees must adopt and execute C-I-CARE standards for all of patients, families and towards each other. C-I-CARE is the foundation of Stanford’s patient-experience and represents a framework for patient-centered interactions. Simply put, we do what it takes to enable and empower patients and families to focus on health, healing and recovery.
You will do this by executing against our three experience pillars, from the patient and family’s perspective:

• Know Me: Anticipate my needs and status to deliver effective care
• Show Me the Way: Guide and prompt my actions to arrive at better outcomes and better health
• Coordinate for Me: Own the complexity of my care through coordination

Equal Opportunity Employer Stanford Health Care (SHC) strongly values diversity and is committed to equal opportunity and non-discrimination in all of its policies and practices, including the area of employment. Accordingly, SHC does not discriminate against any person on the basis of race, color, sex, sexual orientation or gender identity and/or expression, religion, age, national or ethnic origin, political beliefs, marital status, medical condition, genetic information, veteran status, or disability, or the perception of any of the above. People of all genders, members of all racial and ethnic groups, people with disabilities, and veterans are encouraged to apply. Qualified applicants with criminal convictions will be considered after an individualized assessment of the conviction and the job requirements.
Base Pay Scale: Generally starting at $76.84 - $101.82 per hour
The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, specialty and training. This pay scale is not a promise of a particular wage.