Junior Data Scientist
CodeRound is hiring for this role.
- Junior Data Scientist
- Data Scientist
- Applied AI
- Machine Learning
- NLP
- Natural Language Processing
- Python
- Large Language Models
- LLMs
- Generative AI
- Transformer Models
- Deep Learning
- PyTorch
- TensorFlow
- BERT
- RoBERTa
- Model Fine-Tuning
- Model Evaluation
- Model Optimization
- Text Classification
- Information Extraction
- Text Processing
- Embeddings
- Prompt Engineering
- RAG
- LLM Evaluation
- LLM Observability
- Model Monitoring
- Production ML
- ML Deployment
- MLOps
- AWS
- Azure
- GCP
- Docker
- Kubernetes
- Healthcare AI
- Clinical NLP
- Clinical Data
- Medical Coding
- ICD-10
- CPT
- HCPCS
- Revenue Cycle Management
This role is for one of our client companies — a well-funded AI-driven healthcare technology startup that is 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 advanced NLP and Large Language Models to automate clinical coding and improve healthcare revenue operations. As a Junior Data Scientist, you will develop and adapt ML/NLP models for real-world clinical use cases, work with state-of-the-art LLMs, and help translate complex business requirements into reliable, production-ready AI solutions.
What you'll do
- Develop, implement, and maintain Python-based NLP and Machine Learning models for clinical and healthcare applications.
- Work with LLMs and transformer-based architectures to build and adapt AI solutions for domain-specific use cases.
- Evaluate state-of-the-art models and identify suitable approaches based on business requirements, data characteristics, and performance constraints.
- Analyze existing models and customize them to address new problem statements and client-specific requirements.
- Improve model accuracy, reliability, and performance through experimentation, evaluation, and iterative optimization.
- Translate client and business requirements into practical ML/NLP solutions, taking ownership of clearly defined execution tasks.
- Collaborate with cross-functional and client-facing teams to understand use cases and deliver data-driven insights.
- Communicate model outputs, applicability, limitations, and technical trade-offs to technical and non-technical stakeholders.
- Deploy, monitor, and maintain production-grade models, ensuring stability, scalability, and consistent performance.
- Build feedback loops and monitoring mechanisms to continuously evaluate and improve model quality.
- Stay updated with advancements in NLP, LLMs, transformer architectures, and applied Machine Learning techniques.
Must have
2+ years of hands-on experience in Python-based Machine Learning, NLP, or Applied AI. Strong proficiency in Python and experience building, implementing, and maintaining ML/NLP models. Strong understanding of NLP fundamentals, including text processing, tokenization, embeddings, classification, and information extraction. Practical understanding of LLM architectures, capabilities, limitations, and real-world applications. Familiarity with state-of-the-art NLP and LLM models and the ability to adapt them to domain-specific problem statements. Experience evaluating, fine-tuning, or optimizing ML/NLP models to improve accuracy, performance, and reliability. Experience deploying and monitoring ML models in production environments. Strong analytical and problem-solving skills, with the ability to break down ambiguous business problems into actionable technical solutions. Excellent communication skills and the ability to explain model outputs, technical trade-offs, and limitations to technical and non-technical stakeholders.
Good to have
Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow. Experience working with transformer-based architectures and pre-trained models such as BERT, RoBERTa, or domain-specific LLMs. Exposure to LLM evaluation, observability, monitoring, and model performance tracking. Familiarity with prompt engineering, retrieval-augmented generation (RAG), and LLM-based workflows. Experience with cloud platforms such as AWS, Azure, or GCP. Familiarity with Docker, Kubernetes, and containerized model deployment. Exposure to healthcare, clinical notes, medical terminology, or healthcare datasets. Understanding of medical coding systems such as ICD-10-CM, CPT, or HCPCS.