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Jampp is seeking a Product BI Analyst to act as the analytical bridge between ML-driven models and the business. You’ll monitor experiments, surface patterns in auction-level data, and build dashboards with Looker/Tableau to drive clear, actionable insights for advertisers and internal stakeholders.
You’ll work with data scientists and ML engineers on scalable pipelines and ensure results translate into business impact on CPI, CPA, and ROAS, while communicating in English and Spanish.
Jampp is a programmatic advertising platform used by the most ambitious companies to accelerate their mobile businesses. Founded in 2013, Jampp leverages machine learning, creative optimization, and proprietary advertising solutions to drive incremental growth for leading mobile advertisers such as Uber, Just Eat Takeaway.com, FREENOW, Rappi, and BIGO Live.
We’ve experienced tremendous growth over the past years: from expanding our global footprint to 10+ countries and building a team of 100+ mobile experts, to becoming part of the Affle Group, a fast-growing AdTech company that successfully launched its IPO in 2019 and owns multiple industry-leading platforms, including Appnext, mDSP, mediasmart, Newton, RevX, Ultra, and YouAppi, in addition to Jampp.
Today, Jampp is consistently ranked as a top mobile performance partner by the industry’s leading MMPs, featured across 10 categories of Singular’s ROI Index and listed as a top-10 performer in AppsFlyer’s Performance Index.
Data and how it is used play a central role at Jampp and are at the heart of our product, business, operational, and financial decisions.We’re building a new deep learning platform based on shared embeddings — moving from hand-crafted model features to representations our models learn directly from raw data, shared across prediction, bidding, and pricing.
As Product BI Analyst, you’ll join this initiative from day one, working shoulder-to-shoulder with our Data Science and ML teams as the analytical bridge between the models and the business.
Your job is to understand the data, monitor experiments, and bring back the angle the model can’t: which advertisers benefit most from a new model, where we’re underperforming and why, what patterns the numbers reveal that a purely technical view might miss. It’s a highly technical role by BI standards — you’ll be deep in auction-level, high-cardinality data every day — but the value you add is business judgment, not model architecture.