Middle Strong/Senior Data Scientist - Early Campaign Signals PoC

Що робити

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.

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.

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.

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