Sr Data Scientist

Accountabilities

In this role, you will lead efforts to design, optimize, and operationalize advanced ML and LLM systems that power clinical research intelligence platforms, ensuring models are accurate, scalable, and aligned with domain needs. You will:

• Optimize, fine-tune, and evaluate large language models and domain-specific variants using proprietary datasets, including RAG-based architectures

• Assess and select appropriate model architectures (open-source or proprietary) based on performance, scalability, and regulatory requirements

• Build and improve ML pipelines using tools such as Databricks, MLflow, and Delta Lake to support training, evaluation, and deployment

• Collaborate with engineering, product, and clinical teams to translate real-world research challenges into model-driven solutions

• Define and track model performance metrics, including precision, recall, bias, interpretability, and contextual relevance

• Develop reusable frameworks for evaluation, fine-tuning, and monitoring of production AI systems

• Conduct model interpretability and fairness analyses to ensure compliance, transparency, and governance standards

• Stay current with emerging advancements in LLMs, retrieval augmentation, and multi-modal AI, applying innovations to improve performance and efficiency

Requirements

The ideal candidate brings strong experience in applied machine learning, particularly in LLM optimization and production-grade AI systems, combined with the ability to work in complex, regulated data environments. You should have:

• Master’s degree in Machine Learning, Computer Science, or related quantitative field (PhD preferred)

• 5+ years of hands-on experience building, training, and fine-tuning ML or LLM models

• Strong proficiency in Python and experience with deep learning frameworks such as PyTorch

• Practical experience with retrieval-augmented generation (RAG) and embedding-based systems

• Experience working within Databricks environments for ML development and data workflows

• Solid understanding of the end-to-end ML lifecycle, including deployment and monitoring

• Strong analytical thinking, with ability to evaluate model tradeoffs and performance metrics

• Ability to collaborate across technical and non-technical stakeholders, including domain experts

• Clear communication skills, with experience documenting model design, evaluation, and results

• A proactive, research-driven mindset with comfort in fast-paced, iterative development environments

Benefits

• Competitive base salary range: $91,524 – $167,794 annually (based on experience and location)

• Eligibility for performance-based variable bonus

• Remote work flexibility within the United States

• Comprehensive health, dental, and vision insurance

• Paid holidays and additional paid time off

• Retirement savings plan and financial benefits options

• Exposure to cutting-edge AI research in a high-impact, mission-driven environment

• Opportunities for continuous learning and technical advancement

How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
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