Healthcare AI Data Scientist Lead | Predictive Modeling

Remotedxb; Dubai; On-site; AED 300,000 - 550,000; Full time

Job summary

Geisinger is seeking a senior data scientist to lead the end-to-end lifecycle of data science projects, from conceptualization to deployment and optimization, in a healthcare context.

You will build predictive models with ML algorithms, analyze clinical and operational data (Epic Clarity, HL7, DICOM), and translate findings for non-technical stakeholders while mentoring junior scientists.

Qualifications

• Bachelor's Degree in a related field.
• Minimum of 4 years of relevant experience.
• Strong skills in Python, SQL, and C++.
• Expertise in machine learning and statistical methods.
• Ability to perform clinical data cleaning.

Responsibilities

• Lead and manage the entire lifecycle of data science projects from conceptualization to deployment and optimization
• Develop sophisticated predictive models using machine learning algorithms such as decision trees, neural networks, and graphical models
• Utilize clustering, dimension reduction, and deep generative models to analyze complex healthcare datasets
• Extract insights from clinical and operational data sources including Epic Clarity, HL7, and DICOM
• Translate complex technical findings into actionable narratives for non-technical stakeholders
• Mentor and guide junior data scientists to foster professional growth and technical expertise
• Collaborate with cross-functional teams to define project scope and success metrics

Skills

Python

SQL

C++

Machine Learning

Statistical Methods

Clinical Data Cleaning

Education

Bachelor's Degree in a related field

Tools

Epic Clarity

HL7

DICOM

Job description

Geisinger is seeking a senior data scientist to lead the end-to-end lifecycle of data science projects, from conceptualization to deployment and optimization, in a healthcare context.

You will build predictive models with ML algorithms, analyze clinical and operational data (Epic Clarity, HL7, DICOM), and translate findings for non-technical stakeholders while mentoring junior scientists.