Product BI Analyst

Jampp·Spainmid

In short

→Analista BI de producto que traduce modelos de ML en impacto comercial.
→Trabajas con datos a nivel de subasta, alta cardinalidad y grandes volúmenes diariamente.
→Eres el puente entre ciencia de datos y negocio: analizas, cuestionas y propones ideas.

Experiencia con herramientas BI (Looker/Tableau)

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What they ask for

✓4-8 años en análisis de datos, BI, marketing o crecimiento.
✓SQL avanzado con grandes conjuntos de datos.
✓Python a nivel de pandas (análisis, no desarrollo).
✓Experiencia con Looker y/o Tableau.
✓Conocimiento de pruebas A/B y inferencia causal.
✓Experiencia con almacenes de datos en la nube (BigQuery preferido).

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

SQLPythonLookerTableauBigQuerySnowflakeAWSA/B testingMLData pipelines

Who should you write to at Jampp?

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Product BI Analyst WHO WE ARE 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. WHY DO WE NEED YOU? 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. WHAT YOU'LL DO • 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. REQUIREMENTS • 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).

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