Own the design and development of end-to-end intelligent systems combining predictive modeling, LLMs, and domain-specific SLMs
Design and deploy predictive models (e.g., classification, regression, ranking, time series forecasting, anomaly detection, churn and risk modeling, and recommender systems)
Build and fine-tune proprietary SLMs trained on clinical notes and healthcare corpora
Develop and deploy LLM-based and agentic AI systems for reasoning, automation, and decision support
Engineer and optimize models across structured and unstructured data (tabular, text, and image)
Develop advanced clinical NLP solutions using embeddings, semantic search, and domain-specific models (e.g., ClinicalBERT, PubMedBERT)
Extract insights from large-scale unstructured healthcare data, including clinical text corpora
Build and extend multimodal AI capabilities, including computer vision models (e.g., image classification) as complementary systems
Lead end-to-end delivery, from rapid prototyping and MVP development to scalable production systems
Partner with engineering, product, and business stakeholders to align AI solutions with strategic goals
Deploy and monitor ML/LLM/SLM systems, including model performance, drift, bias, and impact
Implement MLOps best practices, including CI/CD, model lifecycle management, and scalable cloud deployment (Azure/AWS)
Drive Responsible AI practices, including explainability, governance, and compliance in healthcare environments

Bachelor’s degree in Data Science, Computer Science, Statistics, Engineering, Mathematics, or a related quantitative field, or equivalent practical experience
4+ years of experience in data science, machine learning, or applied AI, with strong foundations in statistical modeling
Proven experience developing and deploying LLM and/or SLM-based systems in real-world environments
Hands-on expertise fine-tuning domain-specific models (e.g., LoRA, QLoRA, RLHF, DAPT, DPO)
Strong experience in predictive modeling, including feature engineering, validation, and production deployment
Experience working with structured and unstructured data, particularly NLP and text-based systems domain-specific models (e.g., ClinicalBERT, PubMedBERT)
Proficiency with modern AI/ML ecosystems, including Hugging Face, PyTorch/TensorFlow, Databricks, and MLOps practices
Ability to independently own and drive complex AI solutions from concept to production

Data Scientist – Applied AI, LLMs, SLMs, Predictive Modeling

Key skills