Role Description Clearco is hiring a Senior Data Scientist to shape the models, experiments, and analytics that drive our risk, underwriting, and revenue decisions. This hands-on senior role sits at the intersection of Data Science, Machine Learning, and Product. You will partner with Engineering, Product, Risk, and Finance to turn ambiguous problems into production-grade models and measurable outcomes that responsibly scale funding for eCommerce businesses. - Design and execute data science experiments, including causal analysis, A/B tests, and offline evaluation. - Develop, evaluate, and iterate on predictive models for credit/risk scoring, revenue forecasting, and policy performance. - Own model performance and monitoring: define success metrics, investigate drift, and drive improvements to data quality and feature reliability. - Partner with Product Engineering to productionize models and analytics with emphasis on reliability, reproducibility, and maintainability. - Perform exploratory data analysis, feature engineering, and robust validation on real-world, messy data. - Communicate insights and recommendations clearly to technical and non-technical stakeholders through documentation and presentations. - Improve analytical standards, code review practices, and documentation to raise technical quality. - Mentor and support team members through pairing, feedback, and sharing best practices. Qualifications - 5+ years of professional experience in data science, applied machine learning, or a related quantitative role. - Strong foundations in statistics and experimentation, including hypothesis testing, causal reasoning, and evaluation design. - Proven experience building and shipping predictive models (classification, regression, time series) and measuring real-world impact. - Strong proficiency in Python and SQL and comfort working with production data workflows. - Experience defining success metrics, aligning with stakeholders, and delivering end-to-end outcomes. - Strong written communication skills and a pragmatic approach to fast-moving environments. - Experience owning model performance, monitoring for drift, and improving feature reliability. Requirements - Experience with credit risk, underwriting, fraud/risk signals, or financial forecasting. - Experience with modern data tooling and warehouses such as BigQuery or Snowflake and transformation frameworks like dbt. - Familiarity with MLOps patterns (model deployment, monitoring, feature stores, orchestration) and cloud environments. - Experience working with messy third-party data sources (banking data, eCommerce platforms, marketing signals).

Role Description The Senior Catastrophe Modeler supports the integrity and effectiveness of catastrophe modeling processes and data within K2. This role is responsible for analyzing and assessing the potential impact of natural and man-made disasters on business operations, while partnering closely with underwriting teams to model individual accounts and develop import templates. The position also involves building and enhancing complex modeling systems, collaborating with cross-functional stakeholders, and delivering strategic insights to help mitigate, optimize, and manage risk across the organization. Key Responsibilities - Take responsibility for the processes which form the basis of exposure and modelling reporting within K2, including maintaining data integrity between warehousing, aggregate, cat modelling and reporting systems. - Maintain catastrophe models to evaluate the potential impact of natural and man-made disasters on our organization's assets, including property, infrastructure, and other relevant areas. - Analyze large datasets, historical data, and scientific research to assess the probability and severity of various catastrophic events, such as earthquakes, hurricanes, floods, and terrorism. - Collaborate with cross-functional teams, including actuaries, underwriters, and risk managers, to understand their requirements and incorporate them into the Exposure Management function. - Validate and calibrate catastrophe models using historical event data and industry benchmarks, ensuring accuracy and reliability. - Provide expertise and guidance on catastrophe risk assessment to senior management, assisting in the development of risk management strategies/policies and portfolio optimization. - Stay updated on the latest advancements in catastrophe modelling techniques, tools, and industry best practices, and recommend enhancements to existing models or implementation of new models as appropriate. - Prepare comprehensive reports, presentations, and visualizations to effectively communicate complex risk assessments and recommendations to stakeholders and external parties. Qualifications - Expertise in RMS and/or Verisk’s AIR catastrophe model vendor platform - Strong technical problem-solving skills with ability to interpret modelled results with business leaders - Excellent verbal and written communication skills - Self-motivated to work independently or in a team as needed; ability to prioritize workload Requirements - College degree in Computer Science, Mathematics, Finance/General Business, or Engineering or equivalent education and/or experience - 4+ years of catastrophic modeling and management reporting experience - Strong Excel, Access, PowerPoint, and SQL knowledge Benefits - Competitive pay - Bonus plan - Medical, dental, and vision insurance - Paid time-off in year of hire - 401(k) with employer match Salary Salary Range: 98,000-120,000 USD/Per year, depending on experience

• Take ownership of the design and execution of data science projects, ensuring high-impact analytics solutions. • Partner with client stakeholders to understand business challenges and develop data-driven strategies. • Oversee data acquisition and integration, ensuring data quality and reliability. • Apply and refine machine learning and predictive modeling techniques to solve complex business problems. • Guide the deployment and integration of predictive models into business applications. • Develop and implement strategies for optimizing model performance and monitoring over time. • Present findings and recommendations to executives, translating complex data concepts into actionable business strategies. • Mentor and support junior and senior team members in data science methodologies and best practices. • Influence data governance and best practices within the organization.

• Develop and maintain data quality monitoring frameworks, including defining key data quality metrics and thresholds • Build and maintain quality dashboards and reports using programming languages (e.g., SQL, Python) and BI tools to provide near real-time study insights • Oversee data validation, coding, reconciliation (e.g., safety, device, external vendor data), and risk-based review processes • Ensure compliance with ICH-GCP, 21 CFR Part 11, HIPAA, and applicable FDA expectations for regulated SaMD trials • Partner with Engineering and Data teams to support ingestion, transformation, and traceability of multi-source clinical datasets (e.g., wearable data, ePRO, device integrations, CRO exports) • Manage CRO and vendor data management deliverables, ensuring sponsor oversight and quality standards are met • Identify data-related risks early and drive mitigation strategies • Contribute to SOP development and help standardize scalable, repeatable data management practices across Research Operations