Lead Data Scientist
Salary undisclosed
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10+ years of experience as a technical product or program manager, product owner, solutions
architect, or similar role, preferably in a data science or analytics environment.
Strong technical background, including knowledge of data science methodologies, tools, and
technologies (e.g., Python, R, SQL, Machine Learning frameworks).
Experience working with cloud platforms (e.g., Azure, Google Cloud Platform, AWS).
General understanding of data engineering concepts
Experience with agile project management methodologies (e.g., Scrum).
Preferred Qualifications
Advanced degree in a relevant field, such as data science, computer science, or healthcare
management.
Experience with scientific research and analysis tools like Jupiter, Studio, Viscose.
Experience delivering products and machine learning models using cloud-native services like
Big Query, Daturic, Redshift, Sage maker, and Synapse.
Developing products in regulated environments, particularly healthcare.
Machine learning, model development, Mops, and deep learning frameworks (e.g.,
TensorFlow or PyTorch)
Working with electronic health record data.
Familiarity with Data Visualization tools and techniques
Experience in Healthcare, health research, life sciences, or similar industries.
Required Knowledge, Skills and Abilities
Strong leadership and management skills, with the ability to provide guidance and support to
a team of engineers
Excellent communication, interpersonal, problem-solving, and analytical skills.
Knowledge and understanding of healthcare informatics related technologies and standards.
Proven ability to manage complex projects with multiple stakeholders and competing
priorities.
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
architect, or similar role, preferably in a data science or analytics environment.
Strong technical background, including knowledge of data science methodologies, tools, and
technologies (e.g., Python, R, SQL, Machine Learning frameworks).
Experience working with cloud platforms (e.g., Azure, Google Cloud Platform, AWS).
General understanding of data engineering concepts
Experience with agile project management methodologies (e.g., Scrum).
Preferred Qualifications
Advanced degree in a relevant field, such as data science, computer science, or healthcare
management.
Experience with scientific research and analysis tools like Jupiter, Studio, Viscose.
Experience delivering products and machine learning models using cloud-native services like
Big Query, Daturic, Redshift, Sage maker, and Synapse.
Developing products in regulated environments, particularly healthcare.
Machine learning, model development, Mops, and deep learning frameworks (e.g.,
TensorFlow or PyTorch)
Working with electronic health record data.
Familiarity with Data Visualization tools and techniques
Experience in Healthcare, health research, life sciences, or similar industries.
Required Knowledge, Skills and Abilities
Strong leadership and management skills, with the ability to provide guidance and support to
a team of engineers
Excellent communication, interpersonal, problem-solving, and analytical skills.
Knowledge and understanding of healthcare informatics related technologies and standards.
Proven ability to manage complex projects with multiple stakeholders and competing
priorities.
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
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
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