Staff Machine Learning Manager

What will you be doing We are looking for a Staff Machine Learning Manager to build and lead the ML function for Accelerate. You will own the entire ML stack - from defining the roadmap to hiring the team to shipping models into production. This is not a role where you inherit an existing ML system; you will architect and build the foundational ML capabilities that become the platforms core competitive moat: cross-channel intelligence that autonomously optimizes marketing spend. What You'll Build Cross-Channel Budget Allocation Engine Build optimization models that allocate a brands total marketing budget across channels to maximize aggregate ROAS. Account for channel-specific dynamics: auction mechanics, audience overlap, frequency caps, diminishing returns curves. Move from static allocation to continuous rebalancing based on real-time performance signals. Bid Optimization Pacing Develop bid strategy models that work across platforms with different auction types. Build spend pacing algorithms that distribute budget optimally across time (dayparting, day-of-week, seasonality). Model the response curves (spend vs. conversions) per channel and campaign type. Multi-Touch Attribution Build cross-channel attribution models that go beyond last-click to understand the true incremental value of each channel and touchpoint. Design incrementality testing frameworks to validate attribution models and feed insights back into budget allocation and bid optimization. Performance Forecasting Predict campaign performance (impressions, clicks, conversions, ROAS) before and during campaign execution. Build anomaly detection to flag underperforming campaigns or unusual spend patterns in real-time. Audience Intelligence Build audience segmentation and lookalike modeling that works across channel boundaries. Identify high-value audience segments and optimize targeting recommendations based on historical cross-channel conversion data. Creative Performance Prediction Predict creative asset performance before launch, identify creative fatigue signals, and connect creative attributes (copy, visuals, CTA type) to performance outcomes. What Were Looking For 8+ years in ML/Data Science , with at least 1 year in a tech lead or management role building and shipping ML systems in production Strong ML breadth with depth in at least one of: optimization algorithms, recommender systems, time-series forecasting, causal inference, reinforcement learning, or auction/marketplace ML Hands-on technical leader : You can architect ML systems, review model code, and mentor engineers - not just manage roadmaps Production ML experience : Youve taken models from research to production, dealt with data quality issues, and understand the gap between offline metrics and business impact Ads/MarTech domain experience is a strong plus: bid optimization, budget allocation, attribution, audience targeting, or media mix modeling at an ad platform, DSP, or marketing platform Why This Role Greenfield ML with real data : The platform already has production data flowing from major ad platforms Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.