Staff Machine Learning Manager

Cross-Channel Budget Allocation Engine Build optimization models that allocate a brand's 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.

• 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: You've 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

• Greenfield ML with real data: The platform already has production data flowing from major ad platforms. You're not waiting for data - you're building the intelligence layer on top of a live system
• Clear business impact: Every model you build has a direct line to ROAS improvement for brands. The feedback loop between model and revenue is short
• The problem is genuinely hard: Cross-channel marketing optimization is a multi-objective, partially-observable, adversarial optimization problem. The ad platforms themselves are black boxes. Building intelligence across them is an unsolved problem at the industry level
• Full ownership: You define the ML strategy, hire the team, choose the tools, and ship the models. No inherited tech debt in the ML layer
• Scale potential: InMobi is one of the largest independent ad-tech companies globally. Accelerate is a strategic bet with executive sponsorship

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