Responsibilities

• Lead and develop a team of 4+ ML and Backend engineers including hiring, mentoring, and setting technical direction.
• Own the predictive lifetime value (pLTV) strategy, evolving the core models that guide millions in user acquisition decisions.
• Build production machine learning systems ranging from marketing mix modeling algorithms to real-time forecasting models with millions of predictions per day.
• Drive cross-functional impacts by partnering with Growth, Product, and Finance teams translating business problems into ML solutions.
• Shape technical architecture for MLOps infrastructure, monitoring and rapid iteration cycles.
• Stay hands-on in modeling, architecture decisions, and technical problem-solving as needed.

Qualifications

• 7+ years of applied machine learning experience building and deploying models in production.
• 4+ years managing technical teams like ML engineers and data scientists.
• Expert knowledge of ML fundamentals including supervised/unsupervised learning, time series, causal inference.
• Experience with modern ML frameworks such as PyTorch, TensorFlow, scikit-learn, CatBoost.

• Experience with growth analytics, attribution modeling, and marketing effectiveness.
• Understanding of user acquisition funnels and retention optimization.
• Ability to translate business requirements into technical roadmaps and strong communication skills to explain complex models to executives.

• Experience deploying ML models at scale (millions+ predictions per day).
• Knowledge of MLOps practices including model versioning, monitoring, and automated retraining.
• Understanding of data engineering fundamentals and cloud platforms.

• Nice to have: experience with marketing mix modeling, attribution, ad tech, consumer tech, mobile or health tech, privacy-preserving ML techniques, and A/B testing methodology.

Work Culture and Benefits