Optum Data Scientist Job Openings 2026 – Apply Soon!

Qualifications

Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field
• 1+ years of experience in Data Science, Machine Learning, Analytics, or related domains
• Experience with data analysis and machine learning libraries such as Pandas, NumPy, and Scikit-learn
• Understanding of supervised and unsupervised learning techniques
• Familiarity with data visualization tools such as Tableau, Matplotlib, or Seaborn
• Solid foundation in statistics, probability, and machine learning concepts
• Proficiency in Python and SQL
• Proven solid analytical, problem-solving, and communication skills
• Proven ability to work collaboratively in a cross-functional team environment 

Preferred Qualification:

• Basic understanding of cloud platforms, MLOps, or model deployment concepts

Must have Skills:

• Core Data Science–strong foundation in statistics, probability, ML/DL algorithms, and model evaluation techniques
• Python & SQL–solid proficiency; you think in Python and speak fluent SQL
• ML Toolkit–hands-on experience with Pandas, NumPy, Scikit-learn, and at least one deep learning framework (PyTorch / TensorFlow)
• Generative AI & LLMs–working knowledge of large language models (GPT, LLaMA, etc.), prompt engineering, and prompt optimization techniques
• RAG & Embeddings–understanding of Retrieval-Augmented Generation architectures, vector databases (FAISS, Pinecone, Weaviate), and embedding strategies
• LLM Frameworks–exposure to LangChain, LangGraph, or similar orchestration frameworks
• Communication –ability to distill complex analysis into clear insights for both technical and non-technical audiences
• Mindset–strong problem-solving instincts, intellectual curiosity, and a bias for action.

Good to have skills  :                                                                              

• AI Agents & Autonomous Systems– experience building AI agents, chatbots, or agentic workflows that operate with minimal human intervention
• Multimodal AI– exposure to models spanning text, image, audio, or video (e.g., vision-language models, speech-to-text)
• Model Fine-Tuning– hands-on experience with LLM fine-tuning (LoRA, QLoRA, PEFT) and working with LLM APIs at scale
• MLOps / LLMOps– familiarity with model deployment pipelines, CI/CD for ML, experiment tracking (MLflow, Weights & Biases), and API serving
• Cloud & Containers– working knowledge of AWS (SageMaker, S3, Lambda) and Docker; bonus for Kubernetes exposure
• Real-World Problem Solving– experience with applied use cases like forecasting, optimization, automation, or recommendation systems
• Responsible AI– awareness of AI safety, bias mitigation, hallucination risks, and model governance practices
• Agile & Cross-Functional Collaboration– experience thriving in fast-paced, cross-functional team environments with iterative delivery cycles
• Documentation & Storytelling– ability to maintain clean technical documentation and present findings that drive decisions.