Senior Data Scientist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Scientist based in the United States. This is a senior individual contributor role focused on building production-grade AI, machine learning, and NLP solutions in healthcare. You will own complex initiatives from problem definition and model development through deployment, monitoring, and long-term optimization. The role places a strong emphasis on large language models, event-driven data pipelines, and scalable machine learning systems. You will work closely with engineers, clinicians, pharmacists, and executive stakeholders to turn ambiguous challenges into measurable solutions. A key part of the position is balancing model accuracy, latency, scalability, cost, safety, and regulatory requirements across high-volume healthcare workflows. You will also influence technical direction, mentor other data scientists, and establish engineering and evaluation best practices. This is an opportunity to solve challenging problems at significant scale while helping advance intelligent healthcare technology. 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 through model selection, caching, prompt engineering, and related techniques. Design evaluation datasets and automated evaluation pipelines to measure model quality, error rates, and performance across clinical and financial content, identifying regressions caused by changes in models, prompts, or data. Build and maintain production-grade data foundations, including ETL/ELT pipelines and feature datasets sourced from PostgreSQL transactional systems and Redshift data warehouses. Establish strong standards for data quality, reliability, documentation, lineage, and maintainability across analytical and machine learning data workflows. 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, safety issues, and performance drift. Serve as technical lead on complex, ambiguous projects by defining approaches, establishing best practices, setting technical direction, and influencing broader engineering and data strategy. Coach and mentor data scientists through technical pairing, design reviews, code reviews, and constructive feedback while contributing to technical hiring and team development. Communicate complex analytical results and technical concepts effectively to technical and non-technical audiences, including executive leadership, through presentations, reports, and visualizations. Partner closely with software architects and engineering teams to ensure models and data products integrate effectively into production systems and meet platform requirements. 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 understanding of incremental data loads, idempotency, data quality testing, documentation, and data lineage. Strong software engineering fundamentals, including modular and tested production-quality Python, Git-based workflows, code reviews, Docker, and CI/CD. Experi