Applied Scientist

Job Description Role Overview As an Applied Scientist, you will help design and build intelligent systems that turn data, machine learning, and LLMs into measurable business impact. You will work across the full lifecycle of applied AI problems — from identifying opportunities and formulating hypotheses, to developing models and evaluation frameworks, and iterating based on real-world outcomes. You will partner closely with cross-functional stakeholders to understand user needs, define success criteria, and translate complex technical approaches into actionable insights. Your work will sit at the intersection of science, product, and engineering, with a strong focus on delivering high-impact solutions that improve customer experience and business performance. Key Responsibilities Own business-relevant metrics such as churn reduction, AI attach rate, and seller productivity, and use data and scientific thinking to identify opportunities for improvement Develop a deep understanding of users, their workflows, and pain points, and use those insights to inform what should be built next Define hypotheses, success criteria, and evaluation approaches for AI and ML systems Design, build, and evaluate LLM-powered applications, including agents, RAG systems, text-to-SQL solutions, and recommendation engines Establish robust feedback loops for intelligent systems by instrumenting, measuring, and learning from user and business outcomes Partner with engineers to move prototypes into production, ensuring scientific rigor and practical usability Build and maintain Python-based services and APIs that support AI-driven applications Collaborate on the data infrastructure that powers AI products, including Snowflake, dbt, and Airflow Communicate findings, trade-offs, and recommendations clearly to both technical and non-technical stakeholders Stay current with advances in LLMs, agent architectures, evaluation frameworks, and AI-assisted development workflows What We Are Looking For Education & Experience 3 years of experience in Data Science, Machine Learning, Applied Science, or a related field BA/BS in Computer Science, Data Science, Statistics, Mathematics, or a related discipline Advanced degree is highly preferred Business Acumen & User Focus Experience owning business outcomes through intelligent systems Ability to identify high-impact opportunities and form informed opinions on what to build next Strong user focus: you talk to users, understand their workflows, and let those insights guide your technical decisions Deep understanding of the data → insights → action workflow and how to measure whether systems are driving outcomes Track record of building trusted partnerships with business stakeholders Technical Expertise Hands-on experience building and evaluating ML or LLM-powered applications Strong understanding of experimental design, evaluation frameworks, and model performance measurement Experience with agent architectures, prompt engineering, or similar applied AI techniques Strong Python programming skills for scientific experimentation and production-oriented implementation Strong SQL skills and experience with cloud data warehouses Familiarity with backend API design and modern web application concepts Nice to have: experience with Snowflake, dbt, Airflow, or similar data infrastructure tools Nice to have: experience with AI-assisted development workflows such as Claude Code, Cursor, Copilot, or similar Nice to have: experience with frontend technologies and UX design, particularly React Communication & Collaboration Ability to clearly explain scientific concepts, trade-offs, and recommendations to technical and non-technical audiences Experience working cross-functionally with engineers, analysts, product leaders, and business stakeholders Strong collaboration skills and a continuous learning mindset Why You Will Thrive Here Opportunity to work at the intersection of applied science, AI, ... (truncated, view full listing at source)