Staff Data Scientist
Incrmntal
Staff Data Scientist
Incrmntal
Privacy-first marketing measurement via micro-experiments
Role Description:
As a Staff Data Scientist your role will be to:
Use analytical, statistical and technical skills to understand large, complex datasets with the aim to eliminate waste and measure incrementality for advertisers.
Lead, Develop & improve algorithms for automated decision making around lifetime value, causality, multi-touch attribution, and media mix optimization
Determine, analyze, maintain, and report on core marketing metrics to identify causal relationships between marketing actions and outcomes.
Translate data into actions and recommendations – appropriately interpreting and building on findings, and fully exploiting insights
You will also participate in important decisions around the product, our strategy as well as have the opportunity to choose the areas you work on based on what you want or like to do the most. We believe in helping each other get most of ourselves rather than do what we’re not interested in doing.
Key Requirements:
Min. 8 years of work experience in a commercial data science environment that requires the combined application of statistical & engineering skills
Strong statistical and research skills with a track record of using a variety of math and statistical methods (especially Causal Inference, Prediction models, Bayesian Method)
Excellent development skills (Python required, R or Scala a plus)
Experience in working on big data with distributed computing (Spark, Airflow, Kubernetes)
Familiarity with digital marketing, advertising or analytics is a plus
Extensive experience in the Time Series domain, working with small and large time series frames
Theoretical background knowledge like from a relevant BSc, MSc or PHD is a big advantage (e.g. in Mathematics, Computer Science, Statistics, Physics)
INCRMNTAL helps advertisers measure how different marketing actions affect business results across all channels without using user-level data. It treats each marketing adjustment as a micro-experiment and compares aggregated KPI changes to determine impact. The platform uses causal data science and anomaly detection to spot how campaigns and real-world events shift performance, and it evaluates these changes against shared KPIs to quantify effectiveness. It operates in a privacy-first way, relying on aggregated data and cross-channel measurements instead of individual user data, and requires no coding or SDK. A conversational AI interface translates insights into actionable recommendations, enabling continuous optimization of marketing spend and strategy without interrupting campaigns or requiring separate experiments. Its goal is to help brands optimize their marketing investments with ongoing, data-driven insights across all channels.
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