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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Data Scientist - **Company:** Radnet, Inc. - **Location:** United States (Remote available) - **Experience:** Experienced - **Salary:** $95,000.0 - $150,000.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Amazon Web Services, Data Analysis, Big Data, BigQuery, Health Informatics, Data Mining, Data Visualization, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, NumPy, Tensorflow, Azure Machine Learning, SQL Databases, Feature Engineering, Pytorch, Large Language Models, Apache Spark, Generative AI, Pandas, Matplotlib, Build Management, Scikit Learn, Information Technology, Optimization Algorithms, Data Analytics, Xgboost, Plotly, Machine Learning Operations, Databricks - **Published:** May 31, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=5374b0f8b1e51540 ## About the Role Do you have experience in Time Series Analysis?, Do you have a Master's degree?, * 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. To Ensure Success in This Role, You Must Have: * 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, NumPy, scikit-learn, statsmodels) * Experience with machine learning frameworks (PyTorch, TensorFlow, XGBoost, LightGBM) * Solid foundation in statistics including regression, hypothesis testing, experimental design, and time series analysis * Proficiency in SQL for data extraction and manipulation * Experience with data visualization tools (Matplotlib, Seaborn, Plotly, or BI tools) * Excellent communication skills with ability to explain complex analyses to non-technical stakeholders Preferred * Experience with cloud ML platforms (GCP Vertex AI, AWS SageMaker, Azure ML) * Knowledge of MLOps practices and model deployment pipelines * Healthcare analytics experience including clinical, operational, or revenue cycle domains * Experience with causal inference, Bayesian methods, or optimization techniques * Familiarity with LLMs, NLP, or generative AI applications * Experience with big data technologies (Spark, BigQuery, Databricks) * Track record of deploying models that delivered measurable business impact ## Description * 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 Machine Learning & Model Development * 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 Statistical Analysis & Research * 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 AI Measurement & Effectiveness * 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 Stakeholder Collaboration & Communication * 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 ## Related Videos - [Python Data Visualization @ Deepnote (w/ PyViz overview)](https://www.wearedevelopers.com/videos/113-python-data-visualization-deepnote-w-pyviz-overview) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Vectorize all the things! 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