Senior Data Scientist

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.

Analyze large and complex datasets to extract actionable insights, applying strong statistical foundations, advanced analytics, and domain aware interpretation Design, develop, and deploy machine learning and deep learning models across diverse business and healthcare use cases, ensuring rigor in feature engineering, model training, evaluation, and explainability Build production grade ML solutions using robust, maintainable Python and SQL, with proper versioning, monitoring, performance governance, and scalable deployment patterns Apply classical ML techniques (logistic regression, decision trees, clustering, PCA, Bayesian models, time series models) and advanced methods such as survival analysis and complex statistical modeling Develop deep learning solutions using TensorFlow, PyTorch, Keras, or XGBoost for NLP, speech, image processing, and multimodal workloads; implement CNNs, RNNs, LSTMs/GRUs, and optimization/regularization techniques Work with Hugging Face Transformers, embeddings, sequence models, and NLP/NLU pipelines to support generative and discriminative tasks Build and optimize recommender systems using collaborative filtering, sequence aware models (FPMC, FISM, Fossil), and deep recommendation architectures Explore and integrate graph machine learning techniques and knowledge graphs to model complex entity relationships, reasoning, and advanced analytics Leverage AutoML tools (H2O.ai, Vertex/Google Cloud AutoML, DataRobot) to accelerate experimentation while maintaining scientific rigor and model quality Apply healthcare data literacy to ensure compliant, domain aware model development, including familiarity with ICD, CPT, NDC, SNOMED, LOINC, FHIR, and HL7 datasets Generate synthetic datasets using tools like Gretel.ai or Synthea to support experimentation where real data is limited or sensitive Collaborate with data engineering teams to ensure high quality feature pipelines, correct transformations, and production ready integrations Lead model governance practices, including documentation, model cards, validation reviews, responsible AI considerations, and continuous performance monitoring Communicate insights, methodologies, experiment results, and model implications clearly to both technical and non technical stakeholders Contribute to shared documentation, research notes, and knowledge artifacts using tools like Confluence Stay updated with emerging research, GenAI/ML techniques, and industry trends; proactively bring forward innovations that enhance modeling capabilities 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 regards 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: Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field 6+ years of experience in Data Scientist and relevant stream Hands-on experience with AutoML platforms and deep learning