Overview As a Data Science Advisor on the Enterprise Data Science team, you will build advanced analytics and AI solutions to address healthcare challenges. You will partner with clinical, operations, technology, and business leaders to deliver tools that improve member experience, clinical outcomes, and business growth. You will lead high-impact work from ideation to delivery, shaping how data science is used across the organization. You will work in a fast-paced environment with a focus on accuracy, quality, and practical impact.
Compensation / Benefits- medical, vision, dental
- well-being and behavioral health programs
- 401(k)
- company paid life insurance
- tuition reimbursement
- minimum 18 days of paid time off per year
Responsibilities- Lead design, development, and deployment of predictive modeling, ML, generative AI, and advanced analytics aligned with user needs and business goals.
- Build and refine supervised and unsupervised models using methods like regression, trees, neural networks, clustering, time-series, topic modeling, sentiment analysis, and Bayesian analysis.
- Develop scalable data pipelines and analytics workflows using large healthcare datasets and modern cloud tools.
- Translate complex findings into clear insights, visuals, and actionable recommendations for both technical and non-technical audiences.
- Partner with clinical, operations, technology, and business teams to move projects from discovery to delivery.
- Provide thought leadership on advanced analytics, emerging AI methods, and responsible approaches to improve solution quality and business value.
- Operate with curiosity, sound judgment, and a growth mindset while upholding high standards for accuracy and quality.
Key requirements- Relevant experience in data science, advanced analytics, predictive modeling, ML, or related field.
- Advanced programming skills in Python, R, SQL, SAS or SAS Enterprise Guide, or similar language.
- Hands-on experience building predictive models with regression, decision trees, SVMs, random forests, or neural networks.
- Experience with ML libraries/frameworks such as scikit-learn, MLlib, TensorFlow, or PyTorch.
- Ability to explain complex findings clearly and influence decisions with technical and non-technical partners.
- Strong collaboration, critical thinking, and consulting skills, with the ability to lead complex analytics across teams.
- collaboration
- critical thinking
- consulting skills
- Python
- R
- SQL
