Healthcare Data Scientist: ML & AI for Impact

Molina Healthcare

Northern (KY)

Hybrid

USD 80,000 - 172,000

Full time

14 days+

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Benefits offered by this job

Competitive benefits package

Job summary

Molina Healthcare is seeking a data scientist to perform data and error analysis to improve models, clean and validate data, and run ML experiments across healthcare domains. You will develop and deploy advanced ML models and AI solutions to enhance products and services, leveraging data science, ML, and AI techniques to derive insights from large datasets.

Collaborate with engineers, product managers, and analysts to integrate AI solutions, stay current with AI advancements, and document

Qualifications

Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field.
3+ years’ work experience as a data scientist preferably in healthcare environment but candidates with suitable experience in other industries will be considered.
Knowledge of big data technologies (e.g., Hadoop, Spark).
Technical proficiency in Python and R, and experience with ML frameworks like TensorFlow, Keras, or PyTorch.
Statistical analysis: strong understanding of statistical methods and ML algorithms (k-NN, Naive Bayes, SVM, neural networks).
Experience with Agentic Workflows: designing and implementing agentic workflows for autonomous operations.
RAG Techniques: knowledge of retrieval-augmented generation techniques for improving outputs.
Model Fine-Tuning: experience tuning models for tasks and metrics.
Data Visualization: proficiency with Tableau, Power BI to present data insights.
Database Management: experience with SQL and NoSQL, data warehousing, ETL.

Responsibilities

Extract meaningful insights from complex datasets and inform strategic decisions.
Design, develop, and train ML models using supervised/unsupervised, deep learning, and RL.
Develop and implement agentic workflows with AI agents for autonomous tasks.
Use retrieval-augmented generation to enhance language model outputs.
Fine-tune pre-trained models for specific tasks and datasets.
Data cleaning and preprocessing to ensure high-quality inputs for modeling.
Deploy AI models into production and monitor performance.
Collaborate with software engineers, product managers, and analysts.
Stay current with AI advances to improve models and methodologies.
Document models, methodologies, and results for stakeholders.