• Framing ambiguous problems, defining what success looks like, and translating business challenges into measurable analytical strategies. • Owning and evolving scalable data models, pipelines, and core metrics that serve as the long-term foundations for operational performance in Sales and Membership Operations. • Building and iterating on analytical and modeling solutions, shipping quickly, learning from real-world feedback, and refining systems to drive measurable impact. • Leveraging emerging ML/AI tools thoughtfully, integrating them into production workflows where they add leverage, grounded in strong data fundamentals and clear business outcomes.

• Oversee talent acquisition and recruitment operations, balancing quality, timelines, and stakeholder expectations • Acts as the primary point of contact for study teams, developing evidence-based country and site strategies, enrollment scenarios, and protocol optimization recommendations • Partners cross-functionally (including analytics and recruitment teams) to align feasibility, enrollment planning, and execution strategy throughout the study lifecycle • Delivers early feasibility insights, including initial enrollment timelines, country footprint, competitive landscape, and key assumptions to inform operational planning • Continuously refines country footprint, site strategy, and enrollment assumptions as protocols evolve through feasibility and start-up • Identifies protocol optimization opportunities using real-world data, investigator input, and patient insights • Supports investigator/site identification, database development, and global site intelligence coordination • Leads feasibility outreach, analyzes responses, and provides strategic recommendations for country and site selection • Develops analytics and scenario modeling to optimize site mix, enrollment timelines, diversity goals, and overall study performance • Partners with cross-functional teams to improve study start-up efficiency and recruitment cycle times • Owns delivery against defined KPIs and drives continuous improvement through data, innovation, and streamlined processes • Builds strong stakeholder relationships to enable effective and efficient study execution

• Design and deliver end-to-end predictive models (including partner churn prediction, lead scoring, and partner health scores), translating complex business questions into rigorous, production-ready analytical solutions. • Develop and maintain attribution, marketing mix models and other marketing analytics frameworks that inform resource allocation and campaign strategy. • Apply statistical methods to identify leading indicators, drivers, and patterns across partner data; communicate findings and trade-offs clearly to technical and non-technical stakeholders. • Own the full ML lifecycle, from problem framing and feature engineering through model training, validation, deployment, and monitoring, ensuring models remain accurate, interpretable, and maintainable over time. • Establish and uphold repeatable standards for model development, documentation, and validation that enable consistent, trustworthy DS/ML output across the team. • Identify opportunities to apply ML and data science techniques to new business problems and proactively surface recommendations to leadership. • Build and deploy agentic workflows and Claude Skills that automate analytical and modeling tasks, accelerate DS/ML development, and extend the team's capacity. • Evaluate and recommend emerging AI tools and techniques relevant to data science and analytics. • Act as an AI resource and mentor for the broader analytics team, modeling effective AI-assisted development practices and actively contributing to a shared culture of AI literacy. • Partner closely with Sales, Marketing, and CX stakeholders to understand business context, identify and execute high-value analyses, and translate complex model outputs into clear, actionable insights and business narratives that directly shape organizational strategy and operations. • Bring structure, documentation, and process discipline to ambiguous technical domains, ensuring work is reproducible and knowledge is shared.

Role Description The Senior Scientist supports NOAA CoastWatch by providing scientific, technical, and operational expertise for ocean, coastal, and inland water satellite data products, services, and delivery systems. This role supports NOAA’s mission of making Earth observation data accessible, usable, and impactful for research, operations, and decision-making communities. RIVA Solutions is seeking a Senior Scientist to support NOAA CoastWatch satellite remote sensing programs. The successful candidate will contribute to: - Scientific product development - Satellite data processing - Cloud-enabled data services - Data management modernization - User engagement - Scientific communications - Operational support of NOAA CoastWatch data delivery platforms This role combines scientific expertise, software engineering, data systems knowledge, and stakeholder engagement in support of mission-critical environmental data services. Qualifications - Master’s degree or higher in Oceanography, Remote Sensing, Atmospheric Science, Environmental Science, Computer Science, Engineering, Physics, Mathematics, Information Systems, or a related discipline. - Professional experience supporting satellite remote sensing data processing and environmental data products. - Knowledge of satellite-derived products including sea surface temperature, ocean color, ocean heat content, water surface roughness, vector winds, flood and inundation, sea and lake ice, fused products, and AI-ready data products. - Experience developing, maintaining, or supporting ERDDAP, THREDDS, API servers, or similar scientific data access systems. - Knowledge of HDF, NetCDF-CF, Cloud Optimized GeoTIFF, GCTP, metadata standards, and cloud-optimized Earth science data formats. - At least eight (8) years of scientific programming experience utilizing Python, FORTRAN, C/C++, Perl, shell scripting, Java, JavaScript, MATLAB, IDL, or related technologies. - Proficiency with UNIX/Linux operating environments. - Experience with Linux-based web services, CGI technologies, GIS-compliant internet mapping services, Google Maps, ArcGIS Online, or comparable platforms. - Familiarity with AWS cloud environments and cloud-native approaches for scientific data processing and delivery. - Strong written and verbal communication skills. - Ability to obtain and maintain a Public Trust clearance. Requirements - Experience supporting NOAA, CoastWatch, NESDIS, environmental intelligence, oceanographic, atmospheric, or Earth observation programs. - Experience developing cloud-native scientific data processing workflows and cloud-optimized geospatial data services. - Knowledge of metadata standards, scientific data stewardship, FAIR data principles, and data governance best practices. - Experience supporting scientific visualization, geospatial analysis, and Earth observation imagery products. - Experience with AI-ready environmental data products, machine learning workflows, and data fusion methodologies. - Experience supporting operational scientific systems requiring high availability, reliability, and customer-facing data services. - Experience collaborating with Federal agencies, researchers, and operational users in mission-critical scientific environments. Benefits - Paid Time Off / Sick Leave - Health, Dental, and Vision Coverage - Life Insurance - 401(k) Retirement Plan with Company Match - HSA/FSA Spending Accounts - Long- and Short-Term Disability - Pet Insurance - Wellness Program Initiatives - RIVA Flex (Flexible Hours and Hybrid Support, where applicable)