Senior Data Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Concentrate on comprehending current models, evaluating their performance, choosing the most suitable architectures, and refining them to fulfill specific domain and business requirements, including RAG-based applications.

• Work in close collaboration with data engineering, product, and domain teams to transform real-world research challenges into scalable, model-driven solutions.

• Enhance and fine-tune LLMs and domain-specific variants utilizing proprietary datasets to meet precision and recall objectives.

• Assess model performance against key metrics and benchmarks, recognizing strengths, weaknesses, and areas for improvement.

• Implement and operationalize LLM-based and RAG systems that improve Braid-powered products.

• Partner with data engineering to guarantee scalable and efficient model training, evaluation, and deployment pipelines.

• Evaluate and choose models that are best suited to domain-specific needs.

• Collaborate with clinical and operational specialists to convert research and trial challenges into quantifiable model evaluation frameworks and optimization strategies.

• Perform model interpretability and bias analyses to ensure fairness, transparency, and adherence to governance standards.

• Document methodologies and validation outcomes to support internal governance, reproducibility, and audit preparedness.

• Contribute to reusable fine-tuning workflows, evaluation frameworks, and model monitoring pipelines.

• Remain updated on advancements in LLM optimization, retrieval augmentation, and multi-modal learning.

⛳️ Requirements

• MS in Machine Learning, Computer Science, or a related quantitative field, or equivalent relevant work experience.

• Over 5 years of practical experience in developing and fine-tuning ML or LLM models.

• Proven expertise in Python, along with experience and knowledge of a commercial framework such as PyTorch.

• Practical experience in developing, managing, and troubleshooting workflows within Databricks for data engineering, analytics, and machine learning projects.

• Documented strong understanding of the ML lifecycle.

• Familiarity with embeddings and retrieval-augmented generation (RAG).

🏝️ Benefits

• Health coverage.

• Paid holidays.

• Variable bonus.