Job Description

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.


Primary Responsibilities:

  • Data scientists analyze and interpret complex data to help organizations make informed decisions

  • Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regard to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

    • Required Qualifications:

  • AI Scientist

  • Master’s or Relavant in Computer Science, Machine Learning, or related field

  • 8+ years of experience in AIML engineering and research

  • Experience with experiment tracking tools (e.g., Weights & Biases, MLflow)

  • Hands-on experience with MLOps, model deployment, and monitoring

  • Proven expertise in LLMs, generative AI, and deep learning

  • Solid programming skills in Python and familiarity with ML libraries (e.g., scikit-learn, Keras)

  • Familiarity with AIML governance, ethics, and responsible AI practices

  • Key Skills: Proficiency AutoML: Automated Machine Learning (AutoML) tools like H2O.ai, Google Cloud AutoML, and DataRobot
    Solid statistical and mathematical knowledge
    Deep Learning algorithm techniques, open source tools and technologies, statistical tools, and programming environments such as Python, R, and SQL
    Classical Machine Learning Algorithms like Logistic Regression, Decision trees, Clustering (K-means, Hierarchical and Self-organizing Maps), TSNE, PCA, Bayesian models, Time Series ARIMA/ARMA, Recommender Systems - Collaborative Filtering, FPMC, FISM, Fossil
    Deep Learning algorithm techniques like Random Forest, GBM, KNN, SVM, Bayesian, Text Mining techniques, Multilayer Perceptron, Neural Networks - Feedforward, CNN, LSTM’s GRU’s is a plus.
    Optimization techniques - Activity regularization (L1 and L2), Adam, Adagrad, Adadelta concepts; Cost Functions in Neural Nets - Contrastive Loss, Hinge Loss, Binary Cross entropy, Categorical Cross entropy; developed applications in KRR, NLP, Speech and Image processing
    Deep Learning frameworks for Production Systems like Tensorflow, Keras (for RPD and neural net architecture evaluation), PyTorch and Xgboost, Caffe, and Theono
    Exposure or experience using collaboration tools such as: Confluence (Documentation)
    Synthetic Data Generation: Tools like Gretel.ai and Synthea are used to generate synthetic data, which can be useful for training models when real data is scarce or sensitive

    • At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone - of every race, gender, sexuality, age, location and income - deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.

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