Lead Data Scientist

Your CareerWe are seeking a versatile Data Scientist to join our team and help shape the future of our business operations. This role is a unique hybrid of classical machine learning and applied AI. You will tackle diverse and impactful projects, from building predictive models that drive key business decisions to characterizing and optimizing the performance of our emerging AI agentic systems.You are an entrepreneurial problem-solver who thrives in ambiguous environments and enjoys bridging the gap between technical execution and business strategy. In this role, you will act as both a builder, developing and deploying ML models, and a strategic partner, consulting with business teams to unlock new opportunities with data and AI. If you are passionate about wearing multiple hats, working in a fast-paced setting, and delivering high-quality, impactful work, we want to hear from you.Your ImpactDevelop & Deploy Classical ML Models: Own the end-to-end lifecycle of machine learning projects. You'll build and productionize sophisticated models for critical business areas such as marketing attribution, customer churn prediction, and finance forecastingOptimize AI Agentic Systems: Play a key role in our generative AI initiatives. You will be responsible for characterizing, evaluating, and fine-tuning AI agents—such as conversational systems that allow users to query massive datasets using natural language—to improve their accuracy, efficiency, and reliabilityPartner with Business Stakeholders: Act as an internal consultant to our marketing, product, finance and go-to-market teams. You'll translate business challenges into data science problems, identify opportunities for AI-driven solutions, and present your findings in a clear, actionable mannerOwn the Full Data Science Lifecycle: Your responsibilities will cover the entire project workflow, working with the business to understand the problem, chart a path to solve the problem , feature engineering, model selection and training, robust evaluation, deployment, and in partnership with the data platform team, ongoing monitoring for performance degradation