Product Business Intelligence Analyst, Jampp

• 4-8 years of experience in Data Analytics, BI, Marketing Analytics, or Growth Analytics. We value motivation and analytical sharpness as much as years of experience
• Strong SQL (comfortable with complex queries against large datasets) and working proficiency in Python (pandas-level analysis, not software engineering)
• Experience with BI tools — Looker and/or Tableau
• Experience with cloud data warehouses (BigQuery preferred; Snowflake/AWS also valuable)
• Solid grasp of A/B testing and experimentation fundamentals (significance, sample size, basic causal inference)
• Understanding of how a DSP/ad-tech ecosystem works — auctions, impressions, SSPs, MMPs. Background in a DSP, SSP, ad exchange, or mediation platform is equally welcome
• Comfortable working with high-volume, high-cardinality data
• Good communication skills in both English & Spanish

Desirable

• 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
• Monitor ongoing A/B tests across models and campaigns, going beyond "did it win or lose" into why, and for which advertisers or segments
• Deep-dive into auction-level and campaign data — large volumes, high cardinality (millions of apps, placements, creatives) — to surface patterns the team hasn't already been looking for
• Build and maintain dashboards and reporting frameworks (Looker/Tableau) that give the team clear, actionable visibility into performance
• Translate technical model changes into business impact: what it means for CPI, CPA, and ROAS, and where there's an opportunity to push for more advertiser budget
• Bring proactive ideas to the table. We're not looking for passive reporting — we want insight, brainstorming, and pushback
• Work with automated pipelines (SQL, Python) to keep monitoring and reporting scalable as the team and initiative grow
• Partner closely with Data Scientists and ML Engineers: understand what's being built, flag data quality issues, and validate results from a business lens