Surescripts serves the nation through simpler, trusted health intelligence sharing, in order to increase patient safety, lower costs and ensure quality care. We deliver insights at critical points of care for better decisions - from streamlining prior authorizations to delivering comprehensive medication histories to facilitating messages between providers.

Job Summary:

The Senior Data Solutions Architect is a highly skilled and experienced professional with expertise in data science and artificial intelligence (AI). This role will be responsible for designing, implementing, and managing end-to-end data solutions that leverage advanced analytics and AI techniques to drive business value. This role will collaborate closely with cross-functional teams to understand business requirements, develop scalable data architectures, and lead the implementation of cutting-edge data science and AI solutions. Given our dynamic environment, this role is expected to be an engineering position in that this individual will participate in hands-on coding early proof-of-concept and demonstration work.

Responsibilities:

• Strategic Planning:
  
  • Collaborate with key stakeholders to understand business objectives and translate them into data architecture and AI strategy.
  • Develop roadmaps for implementing data science and AI initiatives aligned with business goals.
  • Translate business needs into data and system requirements and manage information-centric architecture views to quickly visualize data domains and data interactions across the organization.
• Data Architecture Design:
  
  • Design robust and scalable data architectures that support data ingestion, processing, storage, and analysis.
  • Define data models, data flows, and integration patterns to ensure efficient data management and accessibility.
  • Evolve the target state architecture based on stakeholder concerns, business capability requirements, scope, constraints, data governance, security, privacy, and data management principles.
  • Facilitate an architectural roadmap, and influence prioritized backlog, key technologies, design standards, frameworks, and guidelines needed to realize that evolving target state architecture.
• Data Science and AI Implementation:
  
  • Lead the development and deployment of advanced analytics and AI models to extract insights, automate processes, and drive predictive and prescriptive analytics.
  • Utilize machine learning, deep learning, natural language processing, and other AI techniques to solve complex business problems.
• Technology Evaluation and Selection:
  
  • Evaluate emerging technologies, tools, and platforms in the data science and AI space. Aid in early phase preliminary project scoping, feasibility efforts, technology/data solutions, integration, and automation.
  • Make recommendations for adoption based on business requirements, scalability, performance, and cost-effectiveness.
• Cross-Functional Collaboration:
  
  • Day to day collaboration with cross-functional teams across Data & Analytics (e.g., Data/BI/Analytics Engineers and Product Owners), Product Innovation (e.g., Business/Product Analysts), Network Technology and Operations (e.g., Software/Database engineers) and other business stakeholders to ensure seamless integration of data solutions into existing systems and processes.
  • Provide technical guidance and mentorship to team members.
  • Strategic collaboration with Enterprise architecture and engineering, Data Governance, and the Privacy Office.
• Performance Optimization: Optimize data pipelines, algorithms, and models for performance, scalability, and reliability. Conduct performance tuning, troubleshooting, and debugging to ensure efficient operation of data science and AI applications.
• Security and Compliance: Ensure data security, privacy, and compliance with regulatory requirements (e.g., GDPR, HIPAA). Implement best practices for data governance, access control, encryption, and auditability.
• Documentation and Training: Document data architectures, solution designs, and implementation processes. Provide training and knowledge sharing sessions to empower team members and promote best practices in data science and AI.

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
• 12+ years of proven experience as a Data Solutions Architect, Data Scientist, or AI Engineer, with a focus on designing and implementing data-driven solutions
• 3+ years in a technical leadership capacity.
• Expertise in data architecture principles, including data modeling, schema design, ETL processes, and data warehousing.
• Experience working with data management strategy development especially in regards uses of metadata, data lineage, data security, privacy, and data life cycle management.
• Strong proficiency in programming languages such as Python, R, or Java, and experience with data manipulation and analysis libraries (e.g., pandas, NumPy, scikit-learn).
• Hands-on experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and AI technologies (e.g., deep learning, natural language processing).
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Hadoop, Spark, Kafka).
• Knowledgeable of Google Bigquery platform, Bigtable, BQscript, Dataproc, Cloud composer (Airflow), VertrxVertex ML, Spark Scala platform.
• Solid knowledge of various Business Intelligence architecture, data warehouse, data marts, data lake, lakehouse, semantic layer and reporting layers.
• Demonstrated experience working with healthcare data, particularly pharmacy data, including but not limited to prescription records, drug utilization patterns, formulary data, and pharmacy claims.
• Understanding of healthcare regulations and standards (e.g., HIPAA, HITECH) governing the collection, storage, and use of patient health information.
• Strong domain knowledge in healthcare, with a focus on pharmacy operations, medication management, and healthcare analytics. Familiarity with healthcare terminology, workflows, and industry trends.
• Proven track record of applying data science and AI techniques to solve healthcare-related challenges, such as medication adherence, drug safety monitoring, population health management, and healthcare outcomes research.
• Familiarity with pharmacy management systems, electronic health records (EHRs), and other healthcare IT systems commonly used in pharmacy settings. Experience integrating and analyzing data from disparate sources within the healthcare ecosystem.
• Understanding of regulatory requirements specific to healthcare data, including privacy regulations (e.g., HIPAA Privacy Rule) and security standards (e.g., NIST Cybersecurity Framework) applicable to protected health information (PHI).
• Experience collaborating with healthcare providers, payers, pharmaceutical companies, and other stakeholders in the healthcare industry to design and implement data-driven solutions that address their needs and objectives.

• Certifications in data science, AI, or cloud computing (e.g., AWS Certified Solutions Architect, Google Cloud Professional Data Engineer).
• Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).
• Knowledge of DevOps practices and CI/CD pipelines for deploying and managing data science and AI applications.
• Familiarity with agile methodologies and experience working in agile development teams.