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Ontada's Genospace team is transforming precision medicine through advanced analytics, machine learning, and AI-driven insights. We are seeking a Data Scientist to help turn complex healthcare, genomic, and clinical data into actionable insights that support research, clinical development, and patient care.
In this role, you will apply machine learning, statistical modeling, and generative AI techniques to develop predictive models, build analytical solutions, and deliver data-driven recommendations. You'll collaborate closely with product managers, engineers, clinicians, and scientists to advance precision medicine solutions used by leading healthcare organizations.
What You'll Do (Responsibilities)
- Analyze and validate healthcare, clinical, and genomic data to support research and business decisions.
- Build, test, and deploy machine learning models that generate actionable insights.
- Develop AI and large language model (LLM) solutions using prompt engineering techniques.
- Perform statistical analyses and translate findings into clear recommendations.
- Create dashboards, reports, and visualizations for technical and business audiences.
- Partner with product, engineering, clinical, and scientific teams to solve complex data challenges.
- Write production-ready code and support multiple analytics initiatives in a fast-paced environment.
Basic Requirements
- Master’s degree with 2+ years of relevant experience, or bachelor’s degree with 4+ years of relevant experience, or equivalent experience.
- Relevant experience in Data Science, Machine Learning, Analytics, Statistics, or a related field.
- Strong experience programming with Python or R and writing complex queries using SQL.
- Experience applying statistical techniques to analyze data and generate insights.
- Experience creating data visualizations, dashboards, or analytical reports and developing or supporting machine learning models.
Preferred Skills/Experience
- Master's or PhD in Data Science, Statistics, Computer Science, Bioinformatics, or a related quantitative field.
- Experience developing machine learning models using techniques such as NLP, deep learning, Random Forests, or Gradient Boosting.
- Hands-on experience with Generative AI, large language models (LLMs), and prompt engineering.
- Experience working in cloud-based analytics environments, including Azure Databricks or similar platforms.
- Familiarity with development and deployment tools such as Git, Docker, Airflow, or MLOps frameworks.
- Experience working with healthcare, clinical, genomic, oncology, or precision medicine data.
- Knowledge of healthcare data standards, clinical research, EMR data, HIPAA requirements, or related healthcare domains.
Location / Travel
- This position is eligible for a remote work arrangement.
- While Dallas-based employees may be subject to enterprise-level in-office expectations, this role does not have a regular onsite requirement. Minimal travel may be required.
We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here.
Our Base Pay Range for this position
$91,800 - $153,000
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