New Jersey Staffing
Lead Data Scientist - Research and Development - Graph Intelligen...
New Jersey Staffing - Trenton, New Jersey, us, 08628
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Overview
Lead Data Scientist, Research & Development
Highmark Health is seeking a groundbreaking Lead Data Scientist, Research & Development, specializing in Graph Intelligence, who will define the future of how we harness relational insights in healthcare. This is a premier R&D position where you will lead the charge in inventing and proving out transformative graph-native analytical solutions. Your core mission is to push the boundaries of what's possible in advanced analytics by pioneering novel methodologies that leverage network science, knowledge graphs, and Graph Machine Learning (GML) to solve critical problems across the healthcare continuum. Your responsibilities will include designing rigorous experiments, building robust proof-of-concept models, and meticulously evaluating the performance and interpretability of these novel graph algorithms to ensure their real-world applicability. You are not merely a data scientist; you are a relational data innovator with a strategic mindset. You inherently understand that the explicit modeling of entities and their relationships unlocks a deeper layer of intelligence that traditional tabular or sequential data models cannot. Leveraging your profound expertise in graph databases (e.g., Neo4j, ArangoDB, Amazon Neptune, Ontotext GraphDB), distributed graph processing frameworks (e.g., Apache Spark GraphX, Dask-Graph), and leading GML libraries (e.g., PyTorch Geometric, DGL, Spektral), you will conduct in-depth research, construct sophisticated predictive, prescriptive, and diagnostic models directly on graph structures. You will drive initiatives from theoretical concept to validated, scalable prototypes. Your contributions will extend to publishing seminal research findings in top-tier conferences and leading the dialogue on the transformative power of graph intelligence in healthcare. Essential Responsibilities
Work directly with the business to understand their business processes and aims, then identify how analytical solutions could help deliver value for them. This would include being accountable for: outlining complex new use cases, creating high-level impact estimates, identifying data elements needed and where to get them (including proxies), assembling data sets independently using knowledge of Highmark operational and analytic data structures, delivering the analytical solution to several complex business problems simultaneously, documenting objectives, assumptions and processes and enriching/expanding our standards as needed, selecting and applying the appropriate advanced modeling/machine learning techniques to these data sets to deliver business insight, ensuring that the final analysis is well researched, accurate, and documented. Education
Required: Master's degree in Analytics, Mathematics, Physics, Computer and Information Science, Engineering Technology or related field OR Bachelor's Degree + 3 years of relevant work experience in lieu of a Master's Degree. Preferred: Doctoral degree (Ph.D.) in Analytics, Mathematics, Physics, Computer and Information Science, Engineering Technology, or a related field. Experience
Required: 5 years of Data Science + 3 years Data Science (if PhD Education). Preferred: Deep Expertise in Graph Theory & Network Science, Comprehensive understanding of fundamental graph algorithms (centrality, community detection, pathfinding, clustering), knowledge graph principles, and network analysis techniques for complex systems. Advanced Graph Machine Learning (GML), Proven experience designing, implementing, and optimizing various Graph Neural Network (GNN) architectures, graph convolutional networks, and other graph-specific machine learning models for tasks like node classification, link prediction, and anomaly detection in graphs. Knowledge Graph Engineering, Hands-on experience in the entire lifecycle of knowledge graphs, including schema design (ontologies, RDF, OWL), data ingestion, graph construction, data cleaning, entity resolution, and advanced graph querying (e.g., Cypher, SPARQL). Graph Database & Platform Experience, Practical experience with one or more leading graph databases (e.g., Neo4j, Google Spanner Graph) and distributed graph processing frameworks. GML Libraries & Frameworks, Strong command of specialized GML libraries like PyTorch Geometric (PyG), Deep Graph Library (DGL), Spektral, or StellarGraph. Cloud Platform & MLOps, Experience deploying and managing ML models, particularly GML pipelines, in cloud environments (e.g., AWS, Azure, GCP) and familiarity with MLOps principles for research projects. Research & Publication Acumen, A track record of contributing to cutting-edge research, including peer-reviewed publications in top-tier conferences or demonstrated experience in driving novel solution development from concept to prototype. Healthcare Data Familiarity, Understanding of healthcare data domains (claims, clinical, EMR) and related ontologies or standards (e.g., SNOMED CT, ICD) is a significant plus. Experimental Design & Rigor, Demonstrated ability to design robust experiments, rigorously evaluate model performance, interpret complex results, and contribute to the scientific understanding of graph-based solutions. Skills
Analysis of business problems/needs, Analytical and Logical Reasoning/Thinking, Collaborative Problem Solving, Data Analysis with SQL, BigQuery, Statistical Analysis with Python, R, Written & Oral Presentation Skills, Basic proto-typing/front end skills Language (other than English): None Travel Required: 0% - 25% Physical, Mental Demands and Working Conditions
Position Type: Office-Based Teaches / trains others regularly Occasionally Travel regularly from the office to various work sites or from site-to-site Never Works primarily out-of-the office selling products/services (sales employees) Never Physical work site required No Lifting: up to 10 pounds Frequently Lifting: 10 to 25 pounds Occasionally Lifting: 25 to 50 pounds Rarely Disclaimer: The job description has been designed to indicate the general nature and essential duties and responsibilities of work performed by employees within this job title. It may not contain a comprehensive inventory of all duties, responsibilities, and qualifications required of employees to do this job. Compliance Requirement: This position adheres to the ethical and legal standards and behavioral expectations as set forth in the code of business conduct and company policies. Pay Range Minimum: $108,000.00 Pay Range Maximum: $201,800.00 Base pay is determined by a variety of factors including a candidate's qualifications, experience, and expected contributions, as well as internal peer equity, market, and business considerations.
Highmark Health is seeking a groundbreaking Lead Data Scientist, Research & Development, specializing in Graph Intelligence, who will define the future of how we harness relational insights in healthcare. This is a premier R&D position where you will lead the charge in inventing and proving out transformative graph-native analytical solutions. Your core mission is to push the boundaries of what's possible in advanced analytics by pioneering novel methodologies that leverage network science, knowledge graphs, and Graph Machine Learning (GML) to solve critical problems across the healthcare continuum. Your responsibilities will include designing rigorous experiments, building robust proof-of-concept models, and meticulously evaluating the performance and interpretability of these novel graph algorithms to ensure their real-world applicability. You are not merely a data scientist; you are a relational data innovator with a strategic mindset. You inherently understand that the explicit modeling of entities and their relationships unlocks a deeper layer of intelligence that traditional tabular or sequential data models cannot. Leveraging your profound expertise in graph databases (e.g., Neo4j, ArangoDB, Amazon Neptune, Ontotext GraphDB), distributed graph processing frameworks (e.g., Apache Spark GraphX, Dask-Graph), and leading GML libraries (e.g., PyTorch Geometric, DGL, Spektral), you will conduct in-depth research, construct sophisticated predictive, prescriptive, and diagnostic models directly on graph structures. You will drive initiatives from theoretical concept to validated, scalable prototypes. Your contributions will extend to publishing seminal research findings in top-tier conferences and leading the dialogue on the transformative power of graph intelligence in healthcare. Essential Responsibilities
Work directly with the business to understand their business processes and aims, then identify how analytical solutions could help deliver value for them. This would include being accountable for: outlining complex new use cases, creating high-level impact estimates, identifying data elements needed and where to get them (including proxies), assembling data sets independently using knowledge of Highmark operational and analytic data structures, delivering the analytical solution to several complex business problems simultaneously, documenting objectives, assumptions and processes and enriching/expanding our standards as needed, selecting and applying the appropriate advanced modeling/machine learning techniques to these data sets to deliver business insight, ensuring that the final analysis is well researched, accurate, and documented. Education
Required: Master's degree in Analytics, Mathematics, Physics, Computer and Information Science, Engineering Technology or related field OR Bachelor's Degree + 3 years of relevant work experience in lieu of a Master's Degree. Preferred: Doctoral degree (Ph.D.) in Analytics, Mathematics, Physics, Computer and Information Science, Engineering Technology, or a related field. Experience
Required: 5 years of Data Science + 3 years Data Science (if PhD Education). Preferred: Deep Expertise in Graph Theory & Network Science, Comprehensive understanding of fundamental graph algorithms (centrality, community detection, pathfinding, clustering), knowledge graph principles, and network analysis techniques for complex systems. Advanced Graph Machine Learning (GML), Proven experience designing, implementing, and optimizing various Graph Neural Network (GNN) architectures, graph convolutional networks, and other graph-specific machine learning models for tasks like node classification, link prediction, and anomaly detection in graphs. Knowledge Graph Engineering, Hands-on experience in the entire lifecycle of knowledge graphs, including schema design (ontologies, RDF, OWL), data ingestion, graph construction, data cleaning, entity resolution, and advanced graph querying (e.g., Cypher, SPARQL). Graph Database & Platform Experience, Practical experience with one or more leading graph databases (e.g., Neo4j, Google Spanner Graph) and distributed graph processing frameworks. GML Libraries & Frameworks, Strong command of specialized GML libraries like PyTorch Geometric (PyG), Deep Graph Library (DGL), Spektral, or StellarGraph. Cloud Platform & MLOps, Experience deploying and managing ML models, particularly GML pipelines, in cloud environments (e.g., AWS, Azure, GCP) and familiarity with MLOps principles for research projects. Research & Publication Acumen, A track record of contributing to cutting-edge research, including peer-reviewed publications in top-tier conferences or demonstrated experience in driving novel solution development from concept to prototype. Healthcare Data Familiarity, Understanding of healthcare data domains (claims, clinical, EMR) and related ontologies or standards (e.g., SNOMED CT, ICD) is a significant plus. Experimental Design & Rigor, Demonstrated ability to design robust experiments, rigorously evaluate model performance, interpret complex results, and contribute to the scientific understanding of graph-based solutions. Skills
Analysis of business problems/needs, Analytical and Logical Reasoning/Thinking, Collaborative Problem Solving, Data Analysis with SQL, BigQuery, Statistical Analysis with Python, R, Written & Oral Presentation Skills, Basic proto-typing/front end skills Language (other than English): None Travel Required: 0% - 25% Physical, Mental Demands and Working Conditions
Position Type: Office-Based Teaches / trains others regularly Occasionally Travel regularly from the office to various work sites or from site-to-site Never Works primarily out-of-the office selling products/services (sales employees) Never Physical work site required No Lifting: up to 10 pounds Frequently Lifting: 10 to 25 pounds Occasionally Lifting: 25 to 50 pounds Rarely Disclaimer: The job description has been designed to indicate the general nature and essential duties and responsibilities of work performed by employees within this job title. It may not contain a comprehensive inventory of all duties, responsibilities, and qualifications required of employees to do this job. Compliance Requirement: This position adheres to the ethical and legal standards and behavioral expectations as set forth in the code of business conduct and company policies. Pay Range Minimum: $108,000.00 Pay Range Maximum: $201,800.00 Base pay is determined by a variety of factors including a candidate's qualifications, experience, and expected contributions, as well as internal peer equity, market, and business considerations.