• Drive the development of machine learning capabilities (forecasting, recommendation, ranking, optimization, or decision intelligence) powering customer-facing SaaS products. • Design reliable data and feature pipelines alongside models from discovery through experimentation, validation, deployment, and monitoring. • Partner with Product Managers and Software Engineers to embed ML directly into product workflows, user experiences, and decision-making tools. • Move quickly from prototype to production while balancing accuracy, interpretability, latency, maintainability, and business impact. • Define offline and online evaluation strategies, including model quality, drift, and reliability. Design A/B tests and causal measurement frameworks to prove ML features improve customer outcomes. • Collaborate with Data teams to ensure models are supported by high-quality features, while building feedback loops so product experiences improve over time. • Help manage and optimize cloud data infrastructure, ensuring trustworthy insights and proactively managing data health before it impacts users. • Bring strong judgment around when to use traditional ML, statistical modeling, LLMs, heuristics, or simpler product logic. Make practical trade-offs across model complexity and customer impact. • Clearly communicate what ML can and cannot solve to influence roadmap decisions, helping identify where machine learning can create true product differentiation. • Guide and mentor other data scientists, ML engineers, analysts, and cross-functional partners in applied ML best practices.

• Design and execute causal analyses of real-world health data to quantify the impact of medications and interventions on patient outcomes and costs • Develop, validate, and deploy predictive and causal models and AI tools into Arine's medication management platform • Partner with clinicians and HEOR researchers to frame questions as well-defined causal estimands and translate findings into clinical and operational decisions • Create presentations and reports communicating methods, assumptions, and results to the Data Science team and cross-functional stakeholders • Participate in critical reviews within the Data Science team on everything from methods and study design to implementation strategy and code

Role Description As a Senior Data Scientist at Coalition you will be responsible for measuring, understanding, and helping optimize Coalition’s underwriting. You will perform statistical analysis to provide data-driven insights. You will help us understand and improve our cyber risk selection and reduction, pricing and automation in order to grow our revenue in a safe and efficient manner. - Analyze diverse datasets including claims data, cybersecurity risk signals, and underwriting databases to extract meaningful patterns and insights. - Large scale data analysis with the objective of producing valuable risk signals to be used for underwriting or risk evaluation of organizations. - Develop and refine statistical and machine learning models to assess cybersecurity risks with applications to underwriting and pricing. - Create comprehensive reports on underwriting efficiency metrics and risk selection quality to inform strategic decisions. - Apply statistical techniques to evaluate and improve our cyber risk assessment methodology. - Provide analytical support for underwriting strategies to balance growth with risk management. - Identify, analyze, and implement opportunities to automate underwriting workflows using advanced analytics and data-driven strategies. - Collaborate cross-functionally with actuarial, product, and engineering teams to implement data-driven improvements. Lead cross-functional initiatives in area of expertise. - Serve as both a technical lead and a mentor within the underwriting team. - Work alongside other data scientists, software engineers, and cyber security engineers to improve underwriting at Coalition. Intellectual curiosity and proactive approach to identifying improvement opportunities. Qualifications - Bachelor's degree in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Actuarial Science, or related discipline). - 5+ years of experience in underwriting, quantitative analysis, or risk modeling in the insurance industry. - Knowledge of underwriting, pricing, reserving, or risk management processes in insurance. - Advanced SQL skills for querying complex databases and joining disparate data sources. - Expertise in data manipulation and analysis using Python, R, or similar analytical tools. - Experience with data visualization tools (e.g., Tableau, Power BI, Looker). - Experience with dashboard creation for operational metrics tracking. - Strong communication and analytical skills. - Capable of working with remote teams. Requirements - Actuarial qualification (nice-to-have). - Experience in cyber insurance or cyber security (nice-to-have). - Prior experience or willingness to learn how to work with large datasets and big data technologies (Snowflake, AWS Athena) (nice-to-have). Benefits - 100% medical coverage, including outpatient care. - Life insurance. - 25+ paid holidays. - Annual home office stipend. - 7% employer pension contribution. - Mental and physical health wellness programs like Headspace, Wellhub. - Competitive compensation and opportunity for advancement.

• Develop, maintain and enhance ETL/ELT processes for data integration and transformation. • Build, test, troubleshoot and migrate data workflows, mappings and jobs. • Implement and sustain Information Governance processes. • Work on initiatives related to data quality, cataloging, metadata, master data management (MDM), data privacy and data security. • Ensure the reliability, integrity and availability of corporate information.