Lead ML Engineer / Data Scientist

Responsibilities

Build — hands-on, every day

• Design, build, and deploy ML models and pipelines that power core product capabilities: recommendation systems, search relevance, customer segmentation, demand forecasting, and activation optimization
• Develop configurable, multi-tenant model architectures that adapt to different customer contexts, data availability, and business requirements without being rebuilt from scratch
• Engineer production-grade ML systems — not just prototypes. Own model serving, monitoring, retraining, and the infrastructure that keeps models reliable at scale
• Create meaningful models with the data that's actually available — not the data you wish you had. Know how to extract signal from limited, noisy, or sparse datasets
• Design and run rigorous A/B tests and experimentation frameworks — including understanding when A/B testing is insufficient and causal inference methods are required
• Deliver analyses that drive decisions — not dashboards that collect dust. Connect model outputs to business outcomes and communicate them with clarity
• Apply causal reasoning rigorously — know the difference between correlation and causation, design analyses that surface true drivers, and flag when others confuse the two

Lead — set direction and raise the bar

• Define and own the ML roadmap in partnership with the founding team
• Think in systems. Design interconnected systems where recommendation, segmentation, scoring, and activation reinforce each other
• Frame business problems as ML problems — and know when a simpler approach beats a complex model
• Set engineering and scientific standards — validation methodology, experiment design, code quality, reproducibility, and deployment discipline
• Prioritize across competing demands, keeping the team focused on highest-impact work
• Communicate results, tradeoffs, and strategic recommendations clearly to founders, customers, and non-technical stakeholders
• Be the tiebreaker on methodology and architecture

Grow — build the team and the culture

• Hire, mentor, and develop ML engineers and data scientists as the team scales
• Create an environment of scientific rigor without academic slowness — ship, validate, iterate
• Build processes that work at startup speed — reviews and checkpoints that improve quality without killing velocity
• Identify capability gaps and build the team to fill them
• Lead by example: the team sees you in the data, in the code, in the hard problems

Requirements

• ML engineer who ships to production. Write clean, testable Python
• Care about model serving, pipeline reliability, and monitoring — not just offline metrics. Models run in production and own them there
• Systems thinker. Understand how models, data flows, customer behavior, and business outcomes connect
• Product-minded ML leader. Frame every technical decision in terms of the outcome it enables
• Deep B2C business knowledge. Understand problems that consumer businesses face — customer acquisition