Middle Strong_Senior Data Scientist – Early Campaign Signals PoC

A B2B marketing analytics platform for LinkedIn

A B2B marketing analytics platform for LinkedIn advertisers wants to know whether the early signals of an ad campaign can predict its outcome months ahead. Small B2B advertisers close only a handful of deals per year, so standard attribution has too little data to work with. The proof of concept tests a different approach on the platform’s historical data across many advertisers. Engagement and website signals from the first 7-21 days of a campaign are combined into a surrogate index that predicts later pipeline outcomes and states how confident the prediction is.
The PoC is time-boxed to 5-6 weeks and ends with a Go or No-Go decision backed by numbers.

• 5+ years of applied data science or statistics at a senior level.
• Strong command of regression modeling, including regularized and hierarchical (multilevel) models.
• Proven experience with small or sparse datasets and with probability calibration.
• Rigorous validation practice: temporal splits, leakage prevention, overfitting control.Python (pandas, scikit-learn, statsmodels) and SQL.Ability to explain uncertainty to non-technical stakeholders.
• English – upper-intermediate.Nice to have
• Bayesian tooling such as PyMC, Stan or bambi.
• Familiarity with surrogate index and proxy metric methods, such as the work of Athey, Chetty, Imbens and Kang.
• B2B marketing analytics: attribution, account-based marketing, LinkedIn Ads, CRM pipeline data.
• Holdout design, controlled experiments and sequential testing.
• Statistical process control.
• Part-time, 0.5-0.8 FTE, about 20-32 hours per week.5-6 weeks, starting in October 2026, exact date to be confirmed.
• Possible continuation into productization if the PoC succeeds.