Data Scientist

The Data Scientist designs, develops, and deploys advanced analytics and machine learning models to improve healthcare outcomes and operational efficiency within healthcare SaaS platforms. This role collaborates with cross-functional teams to analyze complex datasets, generate actionable insights, and integrate data-driven solutions into secure, scalable, and compliant cloud-native environments. The Data Scientist is responsible for driving innovation in statistical modeling, ensuring responsible AI practices, and supporting the adoption of modern data engineering and visualization best practices.

Key Responsibilities:

1. Advanced Machine Learning & AI Engineering

• Design, develop, and optimize supervised, unsupervised, and reinforcement learning models.

• Implement and fine-tune deep learning architectures using frameworks such as TensorFlow and PyTorch.

• Apply ethical AI principles, including fairness, transparency, privacy, and bias mitigation.

• Deploy and monitor models in production environments, ensuring scalability and reliability.

2. Data Engineering & Cloud-Native Architecture

• Build and maintain scalable data pipelines and ETL processes for real-time and batch analytics.

• Engineer robust data architectures using Spark, MetaFlow, Databricks, and cloud platforms (Azure, AWS, GCP).

• Manage and manipulate large healthcare datasets for model development and analytics.

• Ensure data quality, integrity, and security throughout the analytics lifecycle.

3. Statistical Modeling & Quantitative Analysis

• Apply advanced statistical methods, hypothesis testing, and predictive analytics to healthcare data.

• Design and interpret causal AI experiments to support business and clinical decision-making.

• Develop and validate predictive models for patient outcomes and operational efficiency.

4. Data Visualization & Communication

• Create compelling visualizations using Tableau, Power BI, D3.js, or similar tools.

• Translate complex data and analytics into clear, actionable insights for technical and non-technical stakeholders.

• Communicate findings effectively to engineering, product, and clinical teams.

5. Healthcare Domain Expertise & Compliance

• Ensure solutions adhere to healthcare data standards (HL7, FHIR) and regulations (HIPAA, GDPR, CCPA).

• Work with clinical datasets and understand healthcare workflows to ensure relevance and compliance.

• Stay current with healthcare regulations and data privacy requirements.

6. Collaboration & Continuous Learning

• Work closely with product, engineering, clinical, and compliance teams to deliver integrated, data-driven solutions.

• Share knowledge and mentor team members on data science concepts and tools.

• Commit to continuous improvement and staying current with industry trends and best practices.

Required Qualifications:

Education & Experience Guidelines

• Bachelor's degree in computer science, data science, or other relevant field. 

• 5-8 years of relevant work experience

• Experience developing predictive models and working with healthcare data standards.

• Occasional travel may be required. 

Other Preferred Knowledge, Skills, Abilities or Certifications:

• Cloud Platforms: AWS, Azure, GCP

• AI Tools: Spark, MetaFlow, Databricks

• Healthcare Compliance: HIPAA, GDPR, CCPA

• Healthcare Standards: HL7, FHIR

• AI Ethics: Fairness, transparency, bias mitigation

• Certifications: Azure Data Scientist Associate, Google Cloud Data Engineer, CHDA, IBM Data Science

• Visualization Tools: Tableau, Power BI, d3.js

• Communication: Ability to translate complex data into actionable insights