Clinician Nexus

Data Scientist

• Design, develop, and deploy ML solutions ranging from traditional ML applications (classification, clustering, recommendations) to LLM-based systems, including document parsing, data extraction, RAG pipelines, and LLM agents
• Write clean, maintainable, production-quality Python code that integrates smoothly with existing engineering and deployment infrastructure
• Work with large datasets to clean, preprocess, and analyze data, ensuring data quality and integrity
• Implement and optimize algorithms using best practices in machine learning, deep learning, and statistical analysis
• Collaborate with business stakeholders to understand requirements and deliver data-driven solutions that provide actionable insights
• Develop and maintain scalable pipelines and infrastructure for data processing and model training, versioning, deployment, and monitoring
• Evaluate the performance of machine learning models, including LLM-specific evaluation approaches, and tune models for optimal performance
• Communicate findings, insights, and model performance to both technical and non-technical audiences
• Continuously stay updated on the latest trends, technologies, and best practices

Machine Learning Engineer, Data Scientist

Platform Engineer

• Collaborate with data platform architects to design and specify platform tooling, services, and integrations
• Build data lake and lake house architectures
• Build streaming data architectures to support data platform tooling and services
• Collaborate with software teams to integrate streaming and batch architectures with software products
• Collaborate with data scientists, analysts, and business stakeholders to MVP optimal solutions
• Collaborate with data governance teams and data platform architects to build governance into technical solutions
• Maintain systems and services that provide transparency and observability into our critical systems
• Implement and promote engineering and architectural patterns, perform code reviews, and collaborate in architectural reviews
• Collaborate with product team data engineers and architects to MVP data products, maintain data models, and promote data modeling best practice
• Provide technical leadership and mentorship to product and data engineers, guiding their growth and professional development and enabling their ability to use platform tooling and services
• Lead by example through hands-on contributions to designing, coding, and troubleshooting complex data systems
• Identify and address performance bottlenecks and optimization opportunities within data pipelines, databases, and processing frameworks
• Optimize data processing workflows to improve efficiency and reduce latency
• Lead efforts to diagnose and resolve data-related incidents in a timely manner
• Will participate in the grooming of stories
• Will be responsible for their own tasking towards the completion of stories
• Will mentor junior members of the team and guide junior members on best practice
• Will participate in design
• Will be responsible for the quality of their own code and will participate in code review of others product
• Will be responsible for integrating their own code with the team's DevOps plan and implementation

Machine Learning Engineer, Data Scientist

Data Platform Engineer