Data Scientist

at  Podimetrics

Job Title Data Scientist
Division/Department Data Science & Analytics
Location Remote
Reports to Director, Data Science
Key Relationships Data, Software, Analytics

QUALIFICATIONS:

• Master’s Degree (PhD preferred) in Data Science, Computer Science, Statistics, or related field
• At least 5+ years of SQL experience
• At least 7+ years of hands-on Python experience
• Experience working with unstructured databases, e.g. DynamoDB
• Advanced experience employing image processing techniques, i.e. computer vision, and utilizing tools for medical imaging analysis
• Experience with auditory data analysis and processing techniques for use in sentiment analysis
• Excellent written and verbal communication
• Commitment to capturing and maintaining high quality data
• Dedicated problem solver and self-starter
• Ability to effectively work and collaborate remotely

ROLE DESCRIPTION:

Reporting directly to and working closely with the Director of Data Science, the Data Scientist will serve as a crucial individual contributor to the Data Science & Analytics department. Commensurate with Podimetrics’ mission, the primary objective for the Data Scientist is to improve patients’ lives through the early detection and prevention of diabetic foot complications by employing their skill and expertise in data science practices throughout the organization to support timely, data-driven decision making and develop innovative solutions. This technical and collaborative role will work with members of the Data Science & Analytics teams along with Data Engineering, Software, and R&D to develop advanced recommendation systems, leveraging structured and unstructured data. The ideal candidate will have a strong background in data science methodologies, experience with AWS cloud services, and a passion for transforming healthcare through technology.

KEY RESPONSIBILITIES:

• Data Analysis and Modeling: Conduct sophisticated data analysis and develop models using both structured and unstructured data to inform recommendations.
• Recommendation Engine Development: Design and implement robust recommendation systems to enhance patient care and optimize clinical workflows.
• AWS Cloud Services: Utilize various AWS tools and services to manage, process, and analyze large datasets efficiently.
• Cross-Functional Collaboration: Work closely with clinical experts, engineers, and business leaders to integrate data-driven insights into practical applications and products.
• Innovative Solutions: Explore and identify new data sources and analytical techniques to improve model accuracy and effectiveness.
• Reporting and Visualization: Develop intuitive visualizations and reports to communicate findings and recommendations to both technical and non-technical stakeholders.
• Expertise in machine learning: Develop supervised and unsupervised models for risk stratification and classification of dispositions.
• Expertise in LLMs: Ability to utilize existing LLMs and employ RAG and fine-tuning to optimize LLMs