Senior/Lead Machine Learning Engineer, Flo

• 4+ years of experience in machine learning
• Strong knowledge of classical machine learning and neural networks
• Proven experience deploying ML models in production systems
• Strong computer science foundation
• Strong Python programming skills
• Experience building and managing data pipelines on Big Data using MapReduce or streaming frameworks
• Excellent communication skills and ability to collaborate with diverse teams
• Ability to work in ambiguity and on high-risk systems
• Experience navigating high-impact systems under challenging circumstances

Desirable

• We are Flo, the world’s most popular female health app! Our engineers aren’t just building a better product; they’re making the world a better place by improving female health
• Flo is at the forefront of revolutionizing the AdTech landscape by utilizing advanced machine learning and data analytics
• Our mission is to empower marketing and finance teams with predictive insights that enhance decision-making related to user acquisition (UA), campaign evaluation, budgeting, and more
• We are seeking a talented Machine Learning Engineer to join our dynamic AdTech ML team
• In this role, you will help us develop robust models to predict lifetime value (LTV) and other critical metrics
• Developing ML models end-to-end: from initial experiments to deployment and ongoing maintenance
• Driving company growth in the AdTech domain by building ML solutions, focusing on LTV prediction and other KPIs critical to User Acquisition, Finance, and Marketing teams
• Designing, implementing, and ensuring scalable, production-ready, and high-performance solutions
• Building and maintaining data pipelines for efficient preprocessing, feature engineering, and model training
• Collaborating with key stakeholders, such as UA and Finance teams, to define requirements, interpret predictions, and integrate insights into workflows
• Partnering with product and engineering teams to ensure seamless model integration, deployment, and accuracy in production systems
• Calibrating models to meet business requirements for accuracy, efficiency, and scalability at cohort and individual prediction levels
• Developing and generalizing calibration methodologies to align predictions with cohort-level aggregates
• Exploring and implementing new ML use cases, such as budget allocation optimization, campaign optimization, and spend forecasting
• Establishing monitoring systems for model performance and retraining processes
• Diagnosing and resolving issues in production models