Hands-on Staff Data Scientist role focused on marketing and leadgen: own problem framing, build and validate models (bandits, recommendation/ranking, LTV, funnel/monetization), deploy and monitor ML solutions in AWS, and drive measurable ROAS and revenue impact. Requires 5+ years' applied data science experience and strong Python/SQL skills.

Hands-on role responsible for the full data science lifecycle: framing business problems with stakeholders, building and validating models, and working with ML engineering through deployment and monitoring. Requires 5+ years in applied data science with experience in paid/performance marketing, AWS-based ML deployments, Python, SQL, and techniques such as bandits, recommender/ranking systems, and LTV modeling. Primary metric: ROAS.

Hands-on lead role owning data science end-to-end for a priority vertical (starting with Insurance): problem framing, modeling, deployment handoff to ML engineering, monitoring, and driving ROAS and lead quality improvements. Requires 5+ years applied data science experience and multi-year experience building and deploying ML solutions in AWS; strong Python and SQL skills and experience with reinforcement learning, recommendation/ranking systems, LTV and funnel/monetization optimization.

Hands-on lead applied scientist/ML role focused on performance marketing and lead generation. Responsible for framing business problems with stakeholders, building and validating models (bandits, RL, recommender/ranking, LTV), deploying solutions in AWS, monitoring live ROAS performance, and driving measurable revenue and media efficiency improvements.