Lead Data Scientist

At Memorial Hermann, we pursue a common goal of delivering high quality, efficient care while creating exceptional experiences for every member of our community. When we say every member of our community, that includes our employees. We know that when our employees feel cared for, heard and valued, they are inspired to create moments that exceed expectations, while prioritizing safety, compassion, personalization and efficiency. If you want to advance your career and contribute to our vision of creating healthier communities, now and for generations to come, we want you to be a part of our team.

Job Summary

Leads the analysis of complex and unstructured data sets using advanced statistical methods for use in data driven decision making. Responsible for leading cross-functional teams and providing in-depth data insights for complex business problems approached with advanced analytic techniques to collect, explore, and extract insights from structured and unstructured data. Manages engagement with internal customers on small and medium sized projects. Typically reports to the Manager of Data Science.

Job Description

MINIMUM QUALIFICATIONS

Education:  Bachelor’s Degree in science, engineering, computer science, mathematics, statistics, or related STEM field required. Master’s Degree in Data Science preferred.

Licenses/Certifications:  (None)

Experience / Knowledge / Skills:

• Seven (7) years of experience in data science is required

• Professional experience in hospital setting, medical informatics, healthcare information technology/finance/revenue cycle data management, or Electronic Health Record (EHR) data management is preferred

• Business analytical skills (process flows, procedures, spreadsheets, modeling, etc.), technical expertise, mathematical skills and good understanding of design and architecture principles are required

• Possesses deep understanding of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks

• Proficient understanding of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications

• Ability to communicate, gather requirements and execute storytelling with data

• Possesses advanced level knowledge of the data science project life cycle

• Proficient programming skills in addition to a working knowledge and experience of statistical analysis tools

• Demonstrates proficiency in problem solving, analytical reasoning and decision-making skills

• Demonstrates proficiency in identifying and seeking needed information to perform problem/situation analysis

• Advanced level of understanding and experience in researching and resolving data issues with a logical, instinctive, and problem-solving mentality working with large, complex and incomplete sources

• Exhibits strong project management skills, with an ability to work independently on multiple projects with competing priorities and a strong commitment to meeting goals and deadlines

• Advanced understanding of SQL database management tools

• Exceptional analytical skills and ability to understand and interpret results based on advanced statistical techniques

• Strong written and verbal communication skills in IT and business environments; ability to communicate to technical and non-technical audiences

• Ability to work under minimal supervision in a fast-paced multidisciplinary environment

• Advanced knowledge of data science methods – time series forecasting, linear regression, A/B testing, statistical testing, Clustering, etc.

• Superior customer service in the form of first-rate work products and project management

• Strong ability to manage challenging client situations

• Strong ability to troubleshoot and recommend solutions

• Strong ability to translate complex information for a wide range of stakeholders

PRINCIPAL ACCOUNTABILITIES

• Leads high priority projects that impact the organization.

• Leads complex issues and problems, and refers more complex issues to higher-level staff.

• Provides technical supervision/mentoring to other data scientists and trains the broader audience on data science developments.

• Provides leadership, coaching, and/or mentoring to subordinate group.

• Develops custom data models and algorithms to apply to data sets.

• Develops and applies algorithms or models to ke