Machine Learning Manager

Machine Learning ManagerLocation: United States, RemoteWork Arrangement: Remote ( 4 Day Work Week, Monday-Thursday 10 hours a day)Employment Type: Full-TimeAbout the RoleWe are looking for a Machine Learning Manager to lead a team of machine learning professionals building and improving ML products for fraud prevention and risk decisioning in e-commerce.This is a hands-on, player-coach role. You will spend the majority of your time directly involved in machine learning experimentation, technical decision-making, and driving work from research through production, while also leading and developing a team of 4–7 people.The ideal candidate is someone with a strong technical ML background who has already stepped into people management and wants to continue working deeply in the technical work rather than move into a purely managerial role.Key ResponsibilitiesLead & grow the team: 4 to 7, help them with career development, 1:1s, conflict management, hiring/interviewing, building a culture where negative results are treated as valid progress, not failure.Run a portfolio of experiments, not a delivery queue: partner with tech leads, decide which hypotheses get resourced vs. killed, and bring rigor to validating results before they're called "wins."Balance research bets vs. delivery commitments: manage the trade-off between a committed yearly improvement target and longer-horizon research bets, re-cutting priorities as evidence comes in.Own end-to-end delivery cadence: converge independent experimental workstreams into shippable release candidates, including cutting workstreams that aren't pulling their weight.Set direction from data: operate with high autonomy, defend your team's priorities, and represent results to engineering leadership, Risk, and the wider company.What We're Looking For2+ years of people management experience, ideally 3+ years.Strong technical background in machine learning, data science, statistics, or a related quantitative field.Experience remaining hands-on while managing a technical team.Experience running and evaluating machine learning experiments.Experience making decisions about which technical hypotheses and research efforts should be prioritized.Experience balancing longer-term research initiatives with near-term delivery commitments.Experience driving independent technical workstreams toward production.Experience working toward quantitative goals or measurable improvements.Strong experience with Python and SQL.Experience working with Spark and large-scale data.Comfortable working autonomously and using data to guide technical priorities.Strong ability to work with technical peers and challenge ideas constructively.Preferred QualificationsExperience in fraud, risk, payments, fintech, or e-commerce.PhD in Computer Science, Statistics, or another strongly quantitative discipline.Master's degree in a relevant technical or quantitative field.Experience working with risk or quantitative decisioning systems.Experience leading teams responsible for machine learning products.The Team & EnvironmentYou will join a machine learning organization building the products behind fraud prevention and risk decisioning.The team values technical rigor, experimentation, scientific thinking, and high ownership. Engineers and machine learning professionals are expected to take meaningful ownership of their work and use evidence to determine what should move forward.This is an environment for someone who enjoys being deep in the technical work while also building and developing a strong team.Compensation & BenefitsBase Salary: $200,000–$245,000 annually, depending on locationAnnual Performance Bonus: Up to 10% of base salaryEquity: Stock optionsBenefits: Unlimited PTO, 401(k) match, health, dental and vision insurance, FSA, life and disability insurance, 12 weeks paid parental leave, therapy benefits, and a professional learning budget.Work Authorization & LocationRemote anywhere in the United States.Candidates must be U.S. Citizens or Green Card holders.The team operates across U.S. time zones, with a preference for candidates comfortable working with East Coast hours.