Education: Bachelors or Master's degree in Computer Science, Engineering, Data Science, Statistics, or a related quantitative technical field with a strong foundation in AI/ML principles.
Professional Experience (3-6 years): 3-6 years of progressive experience in data science, machine learning engineering, or a related analytical role.
Minimum 2-3 years directly applying data science methodologies to build and deploy solutions within the US healthcare payer or provider domain.
Technical Proficiency: Python Expertise (3+ years): Advanced proficiency in Python for data manipulation (pandas), machine learning (scikit-learn, TensorFlow/PyTorch), statistical modeling, and data v
SQL Mastery (3+ years): Expert-level SQL skills for querying, joining, and optimizing complex queries across large datasets in cloud data warehouses (e.g., Google BigQuery, Snowflake, Redshift).
Cloud Platform Experience (2+ years): Hands-on experience with at least one major cloud platform (GCP preferred - e.g., BigQuery, AI Platform, Vertex AI; or AWS - e.g., S3, EC2, SageMaker; or Azure -
MLOps Concepts (Foundational): Familiarity with MLOps principles and tools for model versioning, deployment, monitoring, and pipeline orchestration.
Healthcare Payer Domain (Core): Solid understanding of key healthcare payer concepts, data types (e.g., claims, eligibility, clinical codes like ICD/CPT), operational processes, and regulatory require
AI Product Development Mindset: Demonstrated ability to think proactively about how AI/ML can be integrated into products, understand user needs, and contribute to the end-to-end development of AI-dri
Full description
Job Summary Role Summary: As a Data Scientist/Engineer at Highmark, you will play a crucial role in building and deploying innovative native AI solutions that transform the US healthcare payer experience. You will leverage your expertise in data science, machine learning, and engineering principles to extract insights, develop robust models, and integrate them into scalable products. This role requires a proactive mindset, a passion for applying AI to complex healthcare challenges, and the ability to effectively communicate technical concepts to both technical and non-technical audiences.
AI Product Development Implementation: Design, develop, and implement machine learning models and algorithms to solve specific business problems within the healthcare payer domain, contributing directly to native AI product development initiatives.
Data-Driven Insights for Healthcare: Perform in-depth data analysis to identify trends, patterns, and anomalies in large, complex healthcare datasets, translating these insights into actionable recommendations for product enhancements and strategic decisions.
Model Deployment MLOps Support: Contribute to the operationalization of AI/ML models, working closely with MLOps and engineering teams to ensure models are deployed reliably, efficiently, and with appropriate monitoring within our cloud infrastructure.
Collaboration Communication: Collaborate effectively with cross-functional teams including Product Managers, other Data Scientists, Data Engineers, Software Engineers, and business stakeholders to understand requirements, refine solutions, and communicate results.
Proactive Problem Solving: Proactively identify opportunities where AI/ML can drive value, suggest innovative approaches, and contribute to the evolution of our AI capabilities within the payer landscape.
Continuous Learning Adaptation: Stay abreast of the latest advancements in AI, Generative AI, and healthcare technology, actively exploring and applying new techniques to enhance our product offerings.
Essential Responsibilities
AI/ML Model Development: Develop, validate, and refine machine learning models (e.g., predictive, prescriptive, generative) using robust statistical and computational methods, specifically addressing challenges in claims, member engagement, provider management, or clinical operations.
Data Acquisition Feature Engineering: Work with large, diverse healthcare datasets (e.g., claims, clinical, eligibility, provider data) to perform data cleaning, transformation, and sophisticated feature engineering to prepare data for model training.
Programming Scripting: Write clean, efficient, and well-documented code in Python for data processing, model development, evaluation, and integration.
Database Querying Management: Extract and manipulate data from various relational and non-relational databases, optimizing queries for performance and scalability.
Digital Acumen: Experience working with digital products or platforms, understanding API integrations, and contributing to scalable, production-ready solutions.
Communication Stakeholder Engagement (Key): Exceptional Communication Skills: Ability to clearly and concisely articulate complex technical findings, methodologies, and limitations to both technical p
Stakeholder Empathy: Strong ability to listen to and understand business challenges, translate them into data science problems, and provide solutions that resonate with stakeholder needs.
Data Visualization Tools: Experience developing and deploying interactive data visualizations and dashboards using tools like Tableau, PowerBI, Looker, or similar.
Problem-Solving Analytical Rigor: Strong analytical and problem-solving skills, with a meticulous approach to data quality, model validity, and interpretability.
Continuous Learning: A strong interest in applied research and development, a passion for exploring new concepts (especially in Generative AI), and a commitment to continuous learning within the rapid
Model Evaluation Monitoring: Design and implement rigorous model evaluation strategies, track model performance over time, identify potential drift or bias, and contribute to continuous improvement efforts.
Data Visualization Storytelling: Create compelling and interactive data visualizations and dashboards that effectively communicate complex insights and model results to both technical and business audiences.
Cloud Platform Leverage: Utilize and optimize cloud computing resources (e.g., BigQuery, Dataproc, SageMaker, Azure ML) for data storage, processing, and model training/deployment.
Ethical AI Compliance Contribution: Understand and apply principles of ethical AI, data privacy (HIPAA), and security best practices throughout the data science lifecycle, ensuring models are fair, transparent, and compliant.
Documentation Knowledge Sharing: Create comprehensive documentation for models, data pipelines, and analytical processes, contributing to knowledge sharing within the team.
Qualifications
Education: Bachelors or Master's degree in Computer Science, Engineering, Data Science, Statistics, or a related quantitative technical field with a strong foundation in AI/ML principles.
Professional Experience (3-6 years): 3-6 years of progressive experience in data science, machine learning engineering, or a related analytical role.
Minimum 2-3 years directly applying data science methodologies to build and deploy solutions within the US healthcare payer or provider domain.
Technical Proficiency: Python Expertise (3+ years): Advanced proficiency in Python for data manipulation (pandas), machine learning (scikit-learn, TensorFlow/PyTorch), statistical modeling, and data visualization (matplotlib, seaborn, plotly).
SQL Mastery (3+ years): Expert-level SQL skills for querying, joining, and optimizing complex queries across large datasets in cloud data warehouses (e.g., Google BigQuery, Snowflake, Redshift).
Cloud Platform Experience (2+ years): Hands-on experience with at least one major cloud platform (GCP preferred - e.g., BigQuery, AI Platform, Vertex AI; or AWS - e.g., S3, EC2, SageMaker; or Azure - e.g., Data Lake, Synapse, Azure ML).
MLOps Concepts (Foundational): Familiarity with MLOps principles and tools for model versioning, deployment, monitoring, and pipeline orchestration.
Healthcare Payer Domain (Core): Solid understanding of key healthcare payer concepts, data types (e.g., claims, eligibility, clinical codes like ICD/CPT), operational processes, and regulatory requirements (e.g., HIPAA, HITECH).
AI Product Development Mindset: Demonstrated ability to think proactively about how AI/ML can be integrated into products, understand user needs, and contribute to the end-to-end development of AI-driven features.
Digital Acumen: Experience working with digital products or platforms, understanding API integrations, and contributing to scalable, production-ready solutions.
Communication Stakeholder Engagement (Key): Exceptional Communication Skills: Ability to clearly and concisely articulate complex technical findings, methodologies, and limitations to both technical peers and non-technical business stakeholders.
Stakeholder Empathy: Strong ability to listen to and understand business challenges, translate them into data science problems, and provide solutions that resonate with stakeholder needs.
Data Visualization Tools: Experience developing and deploying interactive data visualizations and dashboards using tools like Tableau, PowerBI, Looker, or similar.
Problem-Solving Analytical Rigor: Strong analytical and problem-solving skills, with a meticulous approach to data quality, model validity, and interpretability.
Continuous Learning: A strong interest in applied research and development, a passion for exploring new concepts (especially in Generative AI), and a commitment to continuous learning within the rapidly evolving field of AI in healthcare.