Über
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. The Senior Data Scientist will play a pivotal role in developing and implementing data-driven solutions across various business functions, collaborating with cross-functional teams to analyze complex data sets, derive actionable insights, and drive strategic decision-making. The Senior Data Scientist will design, deliver, and optimize powerful insights and visuals with advanced analytics and AI, including effective use of AI assistants within development tools to accelerate planning, solution design, implementation, and documentation. The Senior Data Scientist is expected to possess expert business, data, and analytics expertise to handle complex advanced analytics initiatives. The Senior Data Scientist is self-motivated and able to work under limited supervision from management, can manage multiple complex and significant projects simultaneously, and is responsible for making connections and integrating work across teams and departments. Working closely with data solutions architects and other leaders, the Senior Data Scientist ensures alignment of prioritization of work and appropriate allocation of resources, while applying responsible, secure, and privacy-aware AI-assisted workflows (human-in-the-loop review, traceability, and validation of AI-generated outputs). This role will lead efforts around process improvement to enhance team effectiveness in leveraging advanced analytics tools and techniques in our Google Cloud environment. In support of the Data Literacy Program, the Senior Data Scientist will be responsible for developing training and information materials on new techniques/tools/reports across the enterprise to ensure rapid and effective adoption of insights derived from advanced analytics techniques. Responsibilities: Data Exploration: Explore and preprocess data from various sources, measuring and ensuring data quality and integrity. Enrich data with external, auxiliary, or commercial data sets to enhance suitability for monetization or AI/ML. Advanced Analytics: Apply statistical and machine learning techniques to collect and analyze large datasets and identify patterns by developing predictive models, deep learning algorithms, and frameworks using tools like TensorFlow and PyTorch. Leverage model farms and other AI repositories for development of innovative data processing pipelines to deliver value to the business. Model Development: Design, build, and validate predictive models to solve business problems. Provide team leadership to ensure provenance and traceability in MLOps development cycles. Data Visualization: Create compelling visualizations to communicate findings and insights to stakeholders. Collaboration: Work closely with business leaders, product managers, data scientists, and engineers to translate business requirements into analytical solutions. AI-Assisted Planning & Design: Use AI assistants to accelerate requirements clarification, solution options, and technical design; convert outputs into reviewed artifacts (design docs, user stories, test plans). AI-Assisted Implementation: Use AI coding assistants to draft code/queries/notebooks/pipelines; perform human review for correctness, security, performance, and maintainability; follow IP/licensing and data-use policies. QA Oversight: Define acceptance criteria, test strategy, and validation methods (data checks; model metrics and bias/robustness as applicable); partner with engineering/QA on regression coverage and release readiness. AI-Teaming & Refinement: Iteratively refine solutions using prompt engineering, grounding/source practices, and evaluation rubrics; document prompts, assumptions, and decisions for repeatability. Mentorship: Provide guidance and mentorship to other data scientists, analysts, and engineers within the team. Serves as a mentor/role model, imparting analytic knowledge, experience, and skills to other staff at all levels, either individually or as a member of project teams. Continuous Learning: Stay abreast of industry trends, emerging technologies, and best practices in data science. Evangelize within company the capabilities and opportunities that data science and advanced analytics empower towards corporate goals. Qualifications: Basic Requirements: Master's degree in Mathematics, Computer Science, Statistics, or other related field; or equivalent experience 5+ years of experience in data science, data management and/or applying data analysis and reporting skills in a business. 5+ years of experience with large healthcare transactional datasets/reporting. 5+ years of experience in healthcare transaction data, including QA, testing, and reporting. Expertise in programming languages to facilitate analysis (e.g. R, Python or MATLAB). Expertise with SQL/PLSQL, relational and NoSQL databases, and structured and unstructured data. Analytical background and research experience with large volumes of personal data. Ability to translate statistical analysis into a written and verbal presentation for non-data science audience. Experience in statistical modeling using healthcare data. Experience developing training material and delivering training to user groups of 10 or more. Knowledge of privacy laws and regulations around health data (HIPAA). Demonstrated proficiency using AI assistants across planning, design, implementation, and documentation (e.g., IDE/code assistants such as GitHub Copilot or Claude Code; chat-based assistants such as Microsoft Copilot; notebook/workbench assistants), with human review, traceability, and validation of AI-generated outputs. Demonstrated experience providing QA oversight for analytics/ML deliverables (defining acceptance criteria, designing validation approaches, partnering with engineering/QA on test automation and release readiness). Experience with AI-teaming techniques for refining technical solutions (prompt engineering, grounding/citation practices, evaluation rubrics and benchmarking, and documentation of AI-assisted decisions), including identifying and mitigating common failure modes (e.g., hallucinations, data leakage, insecure).
Sprachkenntnisse
- English
Hinweis für Nutzer
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