Analytics Data Scientist

Responsibilities

Artificial Intelligence;
Advanced Technology;
The very best in patient care. With decades of expertise, Rad Net is Leading Radiology Forward. With dynamic cross‑training and advancement opportunities in a team‑focused environment, the core of Rad Net’s success is its people with the commitment to a better healthcare experience. When you join Rad Net as an Analytics Data Scientist
, you will be joining a dedicated team of professionals who deliver quality, value, and access in the 21st century and align all stakeholders‑patients, providers, payors, and regulators to achieve the best clinical outcomes.

• Develop analytical models that drive business outcomes.
• Design and build predictive models for forecasting, demand planning, and capacity optimization.
• Develop risk and anomaly detection systems for operational and clinical metrics.
• Create scenario analysis and "what‑if" models to support strategic decision‑making.
• Build decision‑scoring frameworks that quantify trade‑offs and recommend actions.
• Translate business problems into analytical frameworks with measurable outcomes.

• Build, validate, and deploy ML models as enterprise assets.
• Develop feature engineering pipelines using governed data from the Gold Layer.
• Train, validate, and evaluate machine learning models using appropriate techniques and frameworks.
• Implement model monitoring for drift, bias, and performance degradation.
• Create model documentation including methodology, assumptions, limitations, and explainability.
• Partner with AI Engineers to deploy models into production environments.

• Apply rigorous analytical methods to answer business questions.
• Conduct exploratory data analysis to identify patterns, trends, and insights.
• Apply statistical methods (regression, hypothesis testing, time series analysis) to validate findings.
• Design and analyze experiments (A/B tests, randomized trials) to measure intervention impacts.
• Quantify uncertainty and communicate confidence levels in analytical outputs.
• Stay current with advances in data science, ML, and AI methodologies.

• Measure and optimize the impact of AI initiatives.
• Define metrics and KPIs to measure AI model effectiveness and business impact.
• Track and report on model performance in production environments.
• Evaluate AI outputs for accuracy, bias, and fitness for purpose.
  
  Provide feedback to improve AI systems based on real‑world performance.
• Support responsible AI practices including fairness testing and transparency.

• Partner with business teams to deliver analytical value.
• Collaborate with business stakeholders to understand problems and translate them into analytical projects.
• Present findings and recommendations to technical and non‑technical audiences.
• Create visualizations and narratives that make complex analyses accessible and actionable.
• Partner with BI teams to operationalize analytical insights into dashboards and reports.
• Coach and mentor analysts on statistical thinking and advanced analytical techniques.

• Passionate about patient care and exercise sound judgement and an ability to remain professional in all situations.
• You demonstrate effective and professional communication, interpersonal skills and respect with patients, guests & colleagues.
• You have a structured work‑approach, understand complex problems and you are able to prioritize work in a fast‑paced environment.

• Master’s or Ph.D. in Data Science, Statistics, Computer Science, Mathematics, or related quantitative field; or Bachelor’s with equivalent experience.
• 3+ years of experience in data science, machine learning, or advanced analytics roles.
• Strong proficiency in Python and data science libraries (pandas, Num Py, scikit‑learn, stats models).
• Experience with machine learning frameworks (PyTorch, Tensor Flow, XGBoost, Light
  
  GBM).
• Solid foundation in statistics including regression, hypothesis testing, experimental design, and time series analysis.
• Proficiency in SQL for data extraction and manipulation.
• Ex…