Principal Data Scientist - Remote

Primary Responsibilities

• Lead solution architecture and hands on development of machine learning and generative AI applications
• Design, build, and deploy scalable, production grade AI solutions using traditional ML, deep learning, and modern LLM based approaches
• Balance architectural leadership with hands on execution across complex AI/ML initiatives
• Provide technical guidance and mentorship to junior engineers through collaboration and code/design reviews (no people management)
• Partner closely with engineering, product, and cross-functional teams to deliver high impact AI solutions aligned with enterprise standards
• Design, develop, and deploy AI-powered solutions to address complex business challenges
• Lead proof-of-concept experiments in generative AI (transformers, GANs, diffusion models) to solve business problems
• Establish best practices for model governance, versioning, reproducibility and security
• Collaborate with data engineers, data scientists, software engineers and product managers to translate business requirements into technical solutions
• Evaluate emerging tools, libraries and research to drive innovation and maintain competitive edge
• Document architecture designs, conduct design reviews and present technical proposals to stakeholders

Required Qualifications

• 10+ years of experience designing, building, and deploying production machine learning solutions
• Deep expertise in either NLP or Computer Vision, with multiple years of hands on solution ownership in that domain
• Deep expertise in core ML and statistical methods: supervised/unsupervised learning, regression, classification, clustering, time series, Bayesian modeling
• Proven solid foundation in traditional ML and deep learning, demonstrated through substantive work prior to or alongside recent GenAI efforts (GenAI only backgrounds without prior ML depth are not sufficient)
• Demonstrated experience with cloud ML services and infrastructure design on at least one major cloud platform (AWS, Azure or GCP)
• Recent experience (approximately last three years) building GenAI applications using LLMs and frameworks such as LangChain and/or LangGraph
• Hands on programming experience in Python and ML frameworks (e.g., PyTorch, TensorFlow)
• Demonstrated familiarity with big data technologies: Apache Spark, Hadoop, Dask
• Ability to define cloud-native ML infrastructure on Azure, AWS or GCP: containerization (Docker/Kubernetes), ML pipelines (SageMaker, Vertex AI, Azure ML), MLOps (CI/CD, model registry, monitoring)
• Proficiency in deep learning frameworks: TensorFlow, Keras, PyTorch
• Practical experience building or fine-tuning generative models (e.g., GPT, BERT, Stable Diffusion, custom architectures)
• Proven solid background in probability, linear algebra and statistical inference
• Demonstrated track record of moving models from research/POC into production at scale
• Proven excellent problem-solving ability and solid verbal/written communication skills

Preferred Qualifications

• Experience working with healthcare data, systems, or use cases
• Experience with MLOps tools: MLflow, Kubeflow, TFX, Airflow or equivalent
• Proven knowledge of data visualization tools (Tableau, Power BI) and dashboarding
• Work supporting U.S. based healthcare or enterprise environments
• Reside in Minnesota