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 Fortive Corporation Overview Fortive’s essential technology makes the world stronger, safer, and smarter. We accelerate transformation across a broad range of applications including environmental, health and safety compliance, industrial condition monitoring, next-generation product design, and healthcare safety solutions. We are a global industrial technology innovator with a startup spirit. Our forward-looking companies lead the way in software-powered workflow solutions, data-driven intelligence, AI-powered automation, and other disruptive technologies. We’re a force for progress, working alongside our customers and partners to solve challenges on a global scale, from workplace safety in the most demanding conditions to groundbreaking sustainability solutions. We are a diverse team 18,000 strong, united by a dynamic, inclusive culture and energized by limitless learning and growth. We use the proven Fortive Business System (FBS) to accelerate our positive impact. At Fortive, we believe in you. We believe in your potential—your ability to learn, grow, and make a difference. At Fortive, we believe in us. We believe in the power of people working together to solve problems no one could solve alone. At Fortive, we believe in growth. We’re honest about what’s working and what isn’t, and we never stop improving and innovating. Fortive: For you, for us, for growth. About Provation Provation is a leading provider of healthcare software and SaaS solutions for clinical productivity, care coordination, quality reporting, and billing. Our purpose is to empower providers to deliver quality healthcare for all. Provation’s comprehensive portfolio spans the entire patient encounter, from pre-procedure through post-procedure, with solutions for physician and nursing documentation (Provation® MD, Provation® Apex, MD-Reports, Provation® endoPRO®, and Provation® MultiCaregiver), anesthesia documentation (#1 Best in KLAS Provation® iPro), patient engagement, surgical care coordination, quality reporting, and billing capture (Provation® SurgicalValet™), order set and care plan management (Provation® Order Set Advisor™ and Provation® Care Plans), and EHR embedded clinical documentation (Provation® Clinic Note). Provation has a loyal customer base, serving more than 5,000 hospitals, surgery centers, and medical offices, and 700 physician groups globally, including 19 of the top 20 U.S. hospitals. In 2021, Provation was acquired by Fortive Corporation, a Fortune 1000 company that builds essential technology and accelerates transformation in high-impact fields like workplace safety, engineering, and healthcare. For more information about our solutions, visit provationmedical.com and follow us on Twitter, Facebook, and LinkedIn. Our purpose at Provation is to empower providers to deliver quality healthcare for all. To deliver on this commitment, we’re guided by our core values – Provation has a culture of CARES: Community - We have a shared sense of improving healthcare, enriching the broader world we live in and serve. Accountability - We own it and get it done with integrity. Respect - We build diverse teams that collaborate and communicate with positive intent and trust. Excellence - We welcome new ideas as we innovate quality solutions. Service - We are passionate about putting customers first. We Are an Equal Opportunity Employer Fortive Corporation and all Fortive Companies are proud to be equal opportunity employers. We value and encourage diversity and solicit applications from all qualified applicants without regard to race, color, national origin, religion, sex, age, marital status, disability, veteran status, sexual orientation, gender identity or expression, or other characteristics protected by law. Fortive and all Fortive Companies are also committed to providing reasonable accommodations for applicants with disabilities. Individuals who need a reasonable accommodation because of a disability for any part of the employment application process, please contact us at applyassistance@fortive.com.

• Gather, clean, and preprocess data from various sources to ensure its quality and usability for analysis. • Analyze data using statistical methods and tools to identify trends, patterns, and insights that can inform business decisions. • Develop predictive and prescriptive models to forecast future outcomes and optimize business processes. • Create reports and visualizations to effectively communicate findings to stakeholders and support decision-making. • Work closely with business units to understand their data needs and provide actionable insights that drive strategic initiatives. • Monitor and evaluate the performance of implemented models and strategies, adjusting as necessary to improve outcomes. • Conduct ad hoc analyses to support various business needs and answer specific questions as they arise. • Stay updated with the latest trends and technologies in data analytics and continuously improve analytical methods and processes. • Other duties as assigned.

15Five is the AI-powered performance management platform built for business impact. 15Five’s AI-powered all-in-one people management system is easy to use,
delivers effortless insights, and enables managers to lead with impact so that companies and their people can thrive. Within the flow of work, HR leaders are empowered with data-driven insights and recommendations while managers are transformed into change-makers, accelerating engagement, performance, and retention. 15Five combines generative AI, custom analytics, and human-centered principles within a complete platform, including 360° performance reviews, engagement surveys, goal tracking, manager coaching and training, and ongoing feedback tools like guided 1-on-1s and check-ins. A career at 15Five is the chance to have high impact in a fast paced, remote startup environment that operates with focus, urgency, and accountability. Every role and every hire meaningfully impacts our trajectory. We’re a high-performance, high-purpose team that gives you the chance to Build the Future of Work. We run on High Standards and High Support — holding a high bar for results, ownership, clarity, and disciplined execution, while investing in manager enablement, skill development, and whole-person care. We are committed to a No B.S. environment, meaning we anchor ourselves in truth, course-correct in real time, and expect people to lean into direct conversations. We are focused on ensuring every 15Fiver can Work in the Future and that starts with proactively using AI across all aspects of your role to increase leverage, speed, and impact. If you're energized by a fast pace, high standards, and high accountability for yourself and others, you'll thrive here and we encourage you to apply. Staff Data Scientist Role We take pride in our position as an innovator in the Human Resources landscape. This wouldn’t be possible without our outstanding team of engineers that apply next-generation thinking to our software - their abilities allow us to continually elevate the user experience and push leading performance-management technology into the global market. As an experienced Staff Data Scientist, you will play a critical role driving the discovery, creation, and integration of AL/ML models into our performance-management platform at 15Five. You will combine statistical methods with engineering best practices to help meet our business needs. We will rely on your statistical analysis skills, as well as insight for collecting, filtering, and preparing meaningful training data for AI/ML projects. You will be instrumental in modeling HR data and building rich structured context to power our knowledge base as well as AI agents engaging with our customers. You will work closely and collaborate with software and data engineers and guide them on operationalizing your work. You will be a self-starter, and build from the ground up leveraging your experience. Areas of Focus - Applied data science - Statistical analysis - Feature engineering - Supervised/Unsupervised model training & delivery Responsibilities - Data analysis and exploration: Conduct in-depth analysis of large structured and unstructured datasets to uncover patterns, insights, and signals that inform AI models, knowledge representations, and product decisions. - Statistical modeling: Develop and apply statistical methods to identify patterns, trends, and correlations in HR performance, engagement, and behavioral data, supporting evidence-based insights and model development. - Feature engineering and knowledge modeling: Design and engineer features from diverse data sources (e.g., reviews, surveys, behavioral signals, organizational data) to power machine learning models and construct structured AI context layers and knowledge representations used by intelligent systems and agents. - Training data development: Lead the creation and curation of high-quality labeled datasets, including annotation frameworks, evaluation datasets, and ground truth benchmarks that support model training and validation. - Machine learning model development: Design, develop, and train machine learning models (supervised, unsupervised, and representation-learning approaches) for use cases that enhance customer outcomes and enable intelligent product capabilities. - Context layer and AI system support: Develop data structures, embeddings, and context-enrichment pipelines that enable AI agents to retrieve relevant organizational knowledge, behavioral insights, and product context. - Model deployment and integration: Partner with engineering teams to operationalize models in production environments, ensuring scalability, reliability, and alignment with product architecture. - Model evaluation and iteration: Design evaluation frameworks and continuously monitor model performance, iterating on models, data pipelines, and features based on experimentation, feedback, and evolving business needs. - Cross-functional collaboration: Work closely with product, engineering, and design teams to translate business problems into data science solutions and ensure models deliver measurable value. - Documentation and knowledge sharing: Maintain clear documentation for datasets, modeling approaches, evaluation frameworks, and data pipelines to ensure reproducibility, transparency, and knowledge transfer across teams. - AI evaluation and benchmarking: Design evaluation datasets and benchmarking frameworks to measure AI agent performance, including retrieval quality, reasoning accuracy, and contextual relevance. - Data governance and responsible AI: Ensure that data used in models and AI systems meets privacy, security, and ethical standards, particularly when working with sensitive employee performance and engagement data. Required Experience/Competencies/Attributes - Education: BS or higher in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field, or equivalent practical experience. - Industry experience: 7+ years of experience in data science, machine learning, or applied statistics in a production environment. - Statistical expertise: Strong foundation in statistical methods and the ability to apply them to large, complex datasets to identify patterns, trends, and actionable insights. - Machine learning experience: Demonstrated experience developing, evaluating, and deploying machine learning models that drive measurable business impact. Experience with large language models (LLMs), embeddings, or AI-driven systems is a strong plus. - Programming proficiency: Strong programming skills in Python and SQL, with experience building data processing pipelines, training models, and analyzing large datasets. - Data infrastructure experience: Experience working with modern data platforms including data warehouses, relational databases, and distributed data processing systems. - MLOps and production systems: Familiarity with MLOps practices and tools for model training, versioning, deployment, monitoring, and lifecycle management. - Software engineering practices: Understanding of modern software development workflows, including version control (Git), CI/CD pipelines, code review, testing practices, and agile development methodologies. - Communication and collaboration: Strong ability to communicate complex technical concepts to non-technical stakeholders and collaborate effectively with product, engineering, and cross-functional teams. Compensation & Benefits Annual Base Pay Range, US Full-Time Staff Data Scientist: $132,500 - $170,500/year Range may vary based on candidate's home location and proximity to different US compensation zones. This role's Total Rewards package includes participation in an annual discretionary company bonus program, equity awards, and comprehensive benefit and wellbeing offerings. We offer amazing benefits that align with our values and culture at 15Five. Your Health & Wellbeing Medical, dental, vision with 15Five cost subsidy Employer paid Short-Term, Long-Term Disability, and Term LifeFamily planning support with enhanced medical plans and consultation programs Inclusive Benefits Stipend Access to mental health and wellness resources Your Life Flexible Time Off Program Generous paid leave for new parents Military leave Paid Company Holidays Sabbatical Program Your Future 401K with 4% Matching after 6 months Your Work Life Remote work Work with experts in leadership, culture, and personal development For more information see: Our Mission, Vision, & Values -https://www.15five.com/about Our People and Culture -https://www.15five.com/about/careers

• Focus on understanding existing models, assessing their performance, selecting optimal architectures, and fine-tuning them to meet specific domain and business needs—including retrieval-augmented generation (RAG) based applications. • Collaborate closely with data engineering, product, and domain teams to translate real-world research challenges into scalable, model-driven solutions that accelerate Advarra’s vision of a digitally connected research data and technology fabric. • Optimize and fine-tune large language models (LLMs) and domain-specific variants using proprietary datasets to achieve precision and recall targets that drive differentiated customer value. • Evaluate model performance across key metrics and benchmarks, identifying strengths, weaknesses, and opportunities for improvement across predictive, generative, and retrieval-augmented tasks. • Implement and operationalize LLM-based and retrieval-augmented (RAG) systems that enhance Braid-powered products such as Study Design and Site Feasibility. • Collaborate with data engineering to ensure scalable, efficient model training, evaluation, and deployment pipelines using Databricks, MLflow, and Delta Lake. • Assess and select models—open-source or proprietary—that best align with domain-specific requirements and Advarra’s regulated research environment. • Partner with clinical and operational experts to translate research and trial challenges into measurable model evaluation frameworks and optimization strategies. • Conduct model interpretability and bias analyses to ensure fairness, transparency, and compliance with governance standards. • Document methodologies and validation results to support internal governance, reproducibility, and audit readiness. • Contribute to reusable fine-tuning workflows, evaluation frameworks, and model monitoring pipelines within the Braid AI stack. • Stay at the forefront of advancements in LLM optimization, retrieval augmentation, and multi-modal learning, applying new methods that improve scalability, explainability, and cost efficiency