• You're looking for a Data Scientist to join our Data Sciences and ML Engineering team. You'll be building, shipping, and improving the models and LLM-powered systems that sit at the core of our security products. • This is a hands-on role on a small, focused team. You'll have real ownership over the models and pipelines you build, close collaboration with engineering and product, and the runway to go deep on the hard problems. • Your work will span a few areas: • Model development and research. Building classifiers, detectors, and scoring models on messy, high-stakes security data. Designing experiments, evaluating trade-offs, and iterating on architectures — not just hyperparameters. • LLM agent systems. Shaping the prompts, context, tool-use patterns, and supporting content that drive our LLM agents. • Production delivery. Shipping models behind real traffic, monitoring them, and improving them over time. • Evaluation and iteration. Building the evaluation harnesses and feedback loops that let us know whether a change is actually an improvement — often the hardest part of the work. Our models only improve for customers when our evaluations highlight what really matters.

• Liderar a análise de dados e o desenvolvimento de modelos preditivos que irão potencializar nossos produtos e otimizar nossas operações. • Criar, validar e implementar modelos de machine learning para aprimorar a análise de crédito. • Realizar análises profundas para identificar tendências, padrões e oportunidades de negócio, comunicando os resultados de forma clara para as equipes de produto e liderança. • Contribuir ativamente com ideias para aprimorar a coleta, o enriquecimento e a utilização dos dados na plataforma.

• An Applied Data Scientist is responsible for the end-to-end execution of new client engagements utilizing data science, which includes: • Unifying large 1st- and 3rd-party datasets and building predictive models • Deriving clear, actionable, and timely insights from analyses • Creating client-ready materials and solutions for stakeholders of varying technical experience or familiarity with methods • Working with cross-functional teams of data scientists and software engineers where necessary to create and implement solutions • Creatively identifying opportunities in the solution delivery process for scalable applications, and collaborating with other teams to further construct these tools • Maintaining a continuous and independent education of cutting-edge statistical techniques and programming languages

• Lead the development, validation, productionization, and iteration of machine learning models that support city and vehicle tech initiatives, including feature engineering, model selection, performance tuning, and integration into product and enforcement systems • Define and establish model and product performance metrics, building clear evaluation frameworks to assess live performance and guide data-driven decision-making • Own and contribute to the development of analytical data pipelines and instrumentation required to support modeling, experimentation, and reliable reporting • Design, lead, and support experimentation efforts, including product initiatives and pilot programs, to measure impact, validate performance, and deliver insights that inform broader product and business strategy • Manage complex, cross-functional initiatives from scoping through executive communication • Make business recommendations using effective presentation skills, conveying findings at multiple levels including executive leadership, managers, and peers