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Project Description
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.

Client Description
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.

Requirements:
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.

Responsibilities:
Define, together with the client and a business analyst, what counts as campaign success, the outcome window and the cut-off between signals and outcome.
Design the analytical dataset: candidate signals, outcome labels, exclusion rules and safeguards against future information leaking into the signals. A Python data engineer builds the dataset in ClickHouse to this design.
Build the surrogate index as a regularized or hierarchical model, with signal weights shared across advertisers and adjusted per advertiser in proportion to its own data volume.
Validate the model on campaigns it has not seen: temporal backtesting and leave-one-advertiser-out evaluation, with AUC, Brier score and calibration curves.
Compare the model against the current practice of judging campaigns by CTR and CPC, and measure how prediction quality changes between day 14 and 21.
Run error analysis, source ablation and learning curves to show which data is missing for a reliable forecast.
Present weights, patterns and uncertainty to the team and reccomend Go or No-Go for the future phase.

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