Own end-to-end campaign delivery across multiple advertisers; hit KPI targets (CPA, ROAS, retention) and scale when unit economics allow.
Develop audience targeting strategies and supply-side optimization (audience, publisher selection, bid shading, dayparting) across ad formats (banner, video, native, CTV) to unlock incremental scale.
Build and execute testing roadmaps (optimization models, bids, budgets, frequency, creatives, geos, etc.) to drive incremental lift.
Manage pacing, budget allocation, and risk across an account book, proactively mitigating under-delivery.
Understand mobile attribution dynamics (postback flows, attribution windows, deterministic vs probabilistic matching, iOS privacy frameworks) and how they shape bidding strategy, audience targeting, and performance interpretation across MMPs.
Champion process improvements, author tools/queries, and mentor juniors to raise team throughput.
Dive into log-level and aggregated data to diagnose performance; write production-grade SQL (joins, CTEs, window functions) and Python/pandas analyses (or sharp use of AI)
Design and interpret tests, translating findings into the Trading Playbook.
Leverage LLMs (GPT/Claude) to automate recurring analyses (bid curves, cohort reads, anomaly triage), create data briefs, and draft experiments.
Contribute to internal dashboards and trading intelligence tools that accelerate decision-making across the team.
Partner with CSM/Sales on growth plans and QBRs; influence Product with crisp, quantified feedback; collaborate with Supply on inventory quality.
Work with DS/DA on model inputs, new testing hypotheses or model improvements, and with Product Support to quickly unblock delivery.
Requirements
3–5 years of programmatic ad buying experience; mobile in-app DSP experience preferred.
Hands-on with MMPs (AppsFlyer, Adjust, Singular), postback flows, fraud detection, and reporting tools.
Data-driven decision making; comfortable building and modifying SQL queries (joins, CTEs, window functions a plus). Familiarity with BI tools. Familiarity with AI is a must.
Nice-to-have: Python/pandas for ad-hoc analyses and light automation; experience is a plus but not required.
Communication: crisp written analysis and cross-team collaboration. Client-facing confidence preferred.
Tech Stack
iOS
Pandas
Python
SQL
Benefits
Competitive compensation with a performance-based component
Exceptional career growth. Shape trading playbooks, product, mentor junior traders, and influence the tools and analytical frameworks the team builds around you.
A unique opportunity to be part of an experienced team of industry experts and entrepreneurs who bring massive change to the Adtech market
A high degree of responsibility and independence
Direct, day-to-day work with the management
A fun, driven, and multinational team located across Germany, India, Argentina, Ukraine, Spain, the UK and many more countries
A flexible work-from-home arrangement
A 500-dollar home-office setup budget
A 1000-dollar annual learning and development budget