ASSOCIATE DATA SCIENTIST ANALYST

Job Summary: This position utilizes data analysis, data mining, pattern analysis, data visualization, reporting, and data management skills. Supports projects under close supervision of senior data analysts or data scientists. Job Responsibilities: Types of work include descriptive analytics, data query scripting, data analysis, data engineering, visualizations, and forecasting. Provides data insights for business problems that can be approached with analytics techniques to collect, explore, and extract insights from structured and unstructured data. Executes analytical procedures in the framework of a specific project work request. Modifies scripts or software applications to support data management, data extraction and data analysis as required. Leverages communication and interpersonal skills and works with subject matter experts Presents findings in easy to understand terms for the business or clinical practice. Contributes to the interpretation of data analysis and to writing reports. May help customers understand the data set and provide training and suggestions for improvement on the data request. Skills: Required Skills & Experience: Has basic expertise in the data science methods used to analyze data, and knowledge of data types, topics, and scientific challenges and approaches. Demonstrated application of several problem-solving methodologies, planning techniques, continuous improvement methods, and analytical tools and methodologies (e.g. machine learning, statistical packages, modeling, etc.)Incumbent must stay current on healthcare trends and enterprise changes. Interpersonal skills and time management skills. Strong analytical skills and a commitment to customer service. Preferred Skills & Experience: Ability to develop predictive models to address various business problems through leveraging advanced statistical modeling, machine learning, or data mining techniques. Notes: Scope: AI modeling for vascular surgery imaging studies Skills: Programming: Python , SQL, RAI & Machine Learning: Machine Learning, Deep Learning, Generative AI, LLMs, Retrieval-Augmented Generation (RAG), Forecasting, Optimization, Anomaly Detection, Scikit-learn, TensorFlow, XGBoost, Supervised & Unsupervised Learning, Deep Learning, Neural Networks, LSTM Autoencoders, Time-Series Modeling, Predictive ModelingAnalytics: Machine Learning, Predictive Modeling, Segmentation, Statistical Analysis, Time Series Forecasting, Model Governance