Data Science Advisor

Lead the development and implementation of Data Science solutions leveraging predictive models, machine learning, GenAI, and other emerging advanced methods while aligning with user needs, business objectives, and the overall vision.

Responsibilities

• Lead the development and implementation of Data Science solutions leveraging predictive models, machine learning, GenAI, and other emerging advanced methods while aligning with user needs, business objectives, and the overall vision.
• Perform data mining by applying machine learning and supervised learning algorithms.
• Support advanced analytical and data mining efforts including but not limited to clustering, segmentation, logistic and multivariate regression, decision/CART trees, neural networks, time-series analysis, sentiment analysis, topic modeling, and Bayesian analysis.
• Visualize, interpret, report, and communicate data findings using Tableau and other visualization tools.
• Present results and recommendations to non-technical business partners and key leaders to drive decision-making.

Skills & Qualifications

• Master’s degree in any STEM (science, technology, engineering, or math) field plus 1 year of Data Science experience (or a Bachelor’s degree in any STEM (science, technology, engineering, or math) field plus 3 years of Data Science experience)
• experience with healthcare domain
• developing predictive models using analytical methods such as regression, decision trees, support vector machines, Random Forests, and Neural Networks
• SQL
• R
• Python
• Scikit-Learn
• Mllib,TensorFlow,P yTorch,AWS cloud services,running Apache Spark applications,GPT (“Generative Pretrained Transformers”)

Experience

• Requires a Master’s degree in any STEM (science, technology, engineering, or math) field plus 1 year of Data Science experience (or a Bachelor’s degree in any STEM (science, technology, engineering, or math) field plus 3 years of Data Science experience)
• Must have experience with: healthcare domain; developing predictive models using analytical methods such as regression, decision trees, support vector machines, Random Forests, and Neural Networks; SQL; R; Python; Scikit-Learn; Mllib; TensorFlow; PyTorch; AWS cloud services; running Apache Spark applications; and GPT (“Generative Pretrained Transformers”)]
• education[]
• whyJoin[]
• benefits[]
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• careerLevel":"Senior-Level"},{
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