Senior Data Scientist
New
J
JobgetherHealthcare AI
Based in the United StatesFull-TimeSenior
Salary$140,000–$170,000
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSDockerPythonSQLMachine LearningAirflowdbtNLPLLM
Requirements
- Master’s or Ph.D. in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative discipline.
- 5+ years of professional experience in data science, machine learning, and NLP, with a demonstrated track record of delivering models into production environments.
- Advanced proficiency in Python and SQL.
- Hands-on experience developing and orchestrating ETL/ELT pipelines using technologies such as dbt, Airflow, and AWS data services including Glue, DMS, Lambda, and S3.
- Strong software engineering fundamentals, including modular and tested production-quality Python, Git-based workflows, code reviews, Docker, and CI/CD.
- Experience productionizing and monitoring machine learning models in AWS environments using SageMaker and MLflow.
- Demonstrated experience building and evaluating LLM-powered applications, including RAG pipelines and systematic evaluation of output quality.
- Proven ability to independently scope ambiguous technical problems and deliver solutions from conception through production.
- Demonstrated project leadership and experience coaching or mentoring other data scientists.
- Ability to travel approximately 10% for client sites, conferences, or internal meetings.
Responsibilities
- Lead AI, ML, and NLP initiatives end to end, covering problem framing, solution design, model development, validation, deployment, monitoring, and ongoing production maintenance.
- Develop LLM-powered applications that automate complex, high-volume workflows while balancing accuracy, response time, throughput, and inference costs.
- Design evaluation datasets and automated evaluation pipelines to measure model quality, error rates, and performance across clinical and financial content.
- Build and maintain production-grade data foundations, including ETL/ELT pipelines and feature datasets sourced from PostgreSQL transactional systems and Redshift data warehouses.
- Collaborate with clinicians, pharmacists, and other domain experts to establish ground truth, assess edge cases, and validate model behavior against real-world clinical workflows.
- Ensure AI and ML solutions meet appropriate healthcare standards and regulatory requirements, including HIPAA, while monitoring for bias and safety.
- Coach and mentor data scientists through technical pairing, design reviews, code reviews, and constructive feedback.
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