Senior Data Scientist
CodeRound is hiring for this role.
- Senior Data Scientist
- Applied AI
- Machine Learning
- NLP
- Natural Language Processing
- Python
- Large Language Models
- LLMs
- Generative AI
- Transformer Models
- Deep Learning
- PyTorch
- TensorFlow
- Hugging Face
- Model Fine-Tuning
- Model Evaluation
- Model Optimization
- ML System Design
- ML Architecture
- AI Frameworks
- Scalable ML Systems
- Reusable Frameworks
- Production ML
- MLOps
- LLMOps
- LLM Observability
- LLM Monitoring
- RAG
- Vector Databases
- Embeddings
- LLM Orchestration
- Model Deployment
- ML Pipelines
- AWS
- GCP
- Azure
- Docker
- Kubernetes
- Healthcare AI
- Clinical NLP
- Medical Coding
- ICD-10
- CPT
- HCPCS
- Revenue Cycle Management
- Technical Leadership
- Client-Facing Engineering
This role is for one of our client companies — a well-funded AI-driven healthcare technology startup transforming medical coding and revenue cycle management. Backed by $11M in funding from investors including Accel and Together Fund, the company is building a cloud-native AI platform powered by transformer-based Large Language Models trained on millions of clinical notes and claims. As a Senior Data Scientist, you will architect scalable NLP and LLM systems, build reusable AI frameworks, and drive the development of production-grade solutions for complex healthcare use cases.
What you'll do
- Design and architect scalable NLP and LLM frameworks for complex clinical and healthcare applications.
- Build reusable, modular, and extensible AI systems that can support multiple business use cases.
- Define system architecture and design patterns for developing, deploying, and scaling production ML solutions.
- Evaluate state-of-the-art models and adapt them to solve complex, domain-specific problems.
- Lead experimentation and model optimization to improve accuracy, reliability, latency, and scalability.
- Own the complete ML lifecycle, including problem definition, data understanding, experimentation, model development, deployment, monitoring, and optimization.
- Work closely with clients and internal stakeholders to understand business challenges, define success metrics, and translate requirements into scalable AI solutions.
- Communicate model capabilities, limitations, architectural decisions, and technical trade-offs to technical and non-technical stakeholders.
- Establish production best practices for model evaluation, observability, monitoring, and continuous improvement.
- Mentor junior data scientists and engineers, lead design discussions, and drive technical excellence across the team.
- Stay updated with advancements in NLP, LLMs, transformer architectures, and healthcare AI, translating relevant research into production-ready solutions.
Must have
4+ years of hands-on experience in Python-based Machine Learning, NLP, or Applied AI. Strong proficiency in Python, with experience designing and developing complex ML/NLP applications. Deep understanding of NLP concepts, transformer architectures, LLMs, and their practical applications. Proven experience designing scalable ML frameworks, reusable components, and extensible AI systems. Strong experience evaluating, adapting, fine-tuning, and optimizing state-of-the-art models for domain-specific problems. Hands-on experience building and deploying production-grade ML/LLM systems, including model monitoring, evaluation, and optimization. Strong understanding of ML system design, architecture patterns, scalability, reliability, and performance optimization. Experience owning the end-to-end ML lifecycle, from problem framing and experimentation to deployment and continuous improvement. Ability to translate ambiguous business and client requirements into structured technical solutions and define measurable success criteria. Excellent communication and stakeholder management skills, with the ability to explain model capabilities, limitations, and technical trade-offs. Experience mentoring junior team members, leading technical discussions, and establishing engineering best practices.
Good to have
Strong hands-on experience with PyTorch, TensorFlow, and Hugging Face Transformers. Experience with LLM evaluation frameworks, observability, monitoring, and performance tracking. Familiarity with RAG architectures, vector databases, embeddings, and LLM orchestration frameworks. Experience building reusable ML platforms, internal tooling, and automated ML pipelines. Familiarity with cloud platforms such as AWS, GCP, or Azure. Experience with Docker, Kubernetes, and scalable model deployment infrastructure. Exposure to healthcare AI, clinical NLP, medical terminology, or clinical datasets. Understanding of medical coding systems such as ICD-10-CM, CPT, and HCPCS. Prior experience in client-facing, consulting, or enterprise technology environments.