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Senior Programmatic Trader (m/f/d)
- Remote from
- Europe
- Salary
- Undisclosed
- Department
- Marketing & Sales
- Employment
- Full Time
- Experience
- Senior
- Published
- Apply before
- 14 Dec 2026
- Listing views
- 16
- Application actions
- 1
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About this role.
Senior Programmatic Trader (m/f/d)
Berlin, Bangalore, Remote
________________________
Hello👋I am Willian, Trading and Analytics Lead at Kayzen, and I am now looking for a Senior Programmatic Trader to join our team 🙌
About Kayzen
Kayzen is the Apps DSP: an AI-native platform built on its own bidding engine, for buying app inventory across mobile and connected TV screens. App developers, agencies, media buyers and ad platforms use Kayzen to drive full-funnel outcomes across both screens, from awareness and consideration to installs, purchases and calls. Available through self-serve and managed services, Kayzen combines machine-learning optimisation with bid – and impression-level transparency, granular control over buying decisions, and AI capabilities that connect campaign data to the AI assistants teams already use. Kayzen’s vision is to democratise programmatic marketing by making it accessible to every team. Headquartered in Berlin, Kayzen operates globally.
The Team
The Trading team is responsible for creating growth through the management and optimization of mobile performance advertising campaigns for clients utilizing the managed service offering. The goal of the team is to deliver on the client’s objectives through established or new trading best practices and communicate account status regularly and effectively with their CSM counterparts.
Why this role
In this role, you will own and grow a portfolio of mobile programmatic campaigns (UA/RT) across verticals including gaming, fintech, betting, and CTV, delivering CPA/ROAS targets while scaling FCT and protecting contribution margin. You’ll pair deep trading skills with analytics (SQL must have / Python desirable. What matters most: the analytical instinct to ask the right questions and the resourcefulness to find answers, whether through code or AI tools) and LLM-accelerated workflows to move faster than a traditional desk. You won’t just optimize campaigns, you’ll shape the tools, analytical frameworks, and trading playbooks the team uses to win.
What you’ll do
Trading excellence
- 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.
Analytics & automation
- 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.
Cross-functional impact
- 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.
Since I am based in Argentina and other key product and customer success team members are located mostly across EMEA and India, we are open to a remote setup of this position. For optimal overlap with customers’ time zones and internal teams your main working location should preferably be close to the CET time zone.
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.
- Nice-to-have: CTV/OTT programmatic buying experience.
- Solution orientation and drive to succeed.
- Communication: crisp written analysis and cross-team collaboration. Client-facing confidence preferred.
What do we offer?
- 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
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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Sample interview questions
I would first segment performance by publisher, placement, audience, creative, geo, operating system, attribution window, and cohort maturity. I would validate postback integrity and attribution settings, identify where inefficient spend is concentrated, then test bid, budget, targeting, frequency, and supply exclusions while protecting statistically meaningful learning periods.
I would join impression, click, install, and postback data at the appropriate grain, then compare recent performance with a baseline by key dimensions such as app, geo, publisher, creative, device, and hour. I would use CTEs and window functions to calculate trends, detect breakpoints, and distinguish traffic-quality changes from tracking or delivery issues.
Privacy constraints reduce deterministic signal availability and can delay or aggregate conversion data, which makes attribution less granular and increases uncertainty. I would adapt by using appropriate attribution windows, cohort-based analysis, incrementality-aware testing, privacy-safe measurement signals, and more conservative optimization decisions where signal quality is limited.
I scale incrementally, prioritizing supply and audiences with demonstrated marginal efficiency rather than increasing budgets uniformly. I monitor marginal CPA or ROAS, bid curves, pacing, frequency, inventory quality, and revenue contribution, and I stop or adjust scaling when incremental volume no longer meets unit-economics thresholds.
I would use LLMs to accelerate recurring analysis, draft hypotheses, summarize anomalies, and generate query or experiment templates, while keeping data access controlled and validating every output against source data. Decisions affecting bids, budgets, or client reporting should remain traceable, quantitatively verified, and subject to human judgment.
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