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Stealth Talent Solutions · New York
You'll own the investigative science behind the core prediction models at a mobile advertising technology company: finding where models underperform, figuring out why, and turning that into the roadmap the ML engineering team builds against. This isn't a model-building or infrastructure role. You'll diagnose, experiment, and explain, and your work will steer a multi-billion-dollar optimization problem.
Dig into where install-prediction models lose accuracy, slicing by segment, data slice, and campaign type to isolate where and why performance drops
Run calibration checks, feature-importance analysis, and slice-level error analysis to turn a vague performance gap into a precise, testable explanation
Form hypotheses about model behavior and test them empirically (for example, rerunning a pipeline on a reduced sample to test a data-volume theory)
Translate findings into prioritized recommendations for ML engineering, defining the experiment and the expected mechanism, not just the metric
Own production investigations end to end: when a campaign's performance drops, find the root cause (data issues, model shift, prediction drift, privacy-driven signal loss) and report back
Surface longer-term opportunities across the model portfolio, quantifying the potential lift and trade-offs
Keep technical leadership updated with status and new hypotheses without being asked
What you bring
5–7+ years in data science or applied machine learning, with hands-on model diagnostics work
Strong applied statistics and probability, including experiment design
Model calibration and error analysis on production models
Python and SQL
Master's in data science, statistics, applied math, computer science, or a related quantitative field
Comfort explaining why a change moved a metric in terms of weights, loss, and predictions
Self-directed: you take a loosely defined problem and drive it to an answer
Nice to have
PhD
Ad tech or large-scale real-time prediction systems
Class imbalance and feature importance analysis
The details
Hybrid, New York City (onsite 2–3 days a week)
Full-time, permanent
Senior individual contributor / technical lead, no direct reports
U.S. Eastern hours, with daily overlap with a Europe-based team
Immediate start
Details
| Company | Stealth Talent Solutions |
| Location | New York |
| Type | FULL TIME |
| Niche | Systems analysts |