Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Senior Data Scientist (m/f/d)
- Remote from
- EMEA
- Salary
- Undisclosed
- Department
- Data Science & Analytics
- Employment
- Full Time
- Experience
- Senior
- Published
- Apply before
- 13 Dec 2026
- Listing views
- 43
- Application actions
- 3
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About this role.
Europe or India, Remote
_______________________________________________
Hello👋I am Puneet, Co-founder and CPO at Kayzen, and I am now looking for a Senior Data Scientist to join our team 🙌
But wait, you have not heard of Kayzen before?😃
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
You will work with a carefully selected, diverse, senior and globally distributed Data Science team (like Heiko, Chen) on various machine-learning models that process terabytes of data, serving billions of ad requests and users around the globe in real-time. We are highly motivated and aligned in our quest to disrupt the multi-billion dollar ad-tech industry. We would like you to be part of our journey, as our Senior Data Scientist (m/f/d).
Responsibilities
Our day-to-day work in Data Science and Machine Learning encompasses a wide range of activities, including:
- Enhancing Models: Continuously improving existing models by incorporating new features, fine-tuning parameters, and utilizing additional data.
- Algorithm Development: Collaborating with our Machine Learning Engineering team to develop and deploy scalable supervised and unsupervised learning algorithms.
- Data Exploration: Identifying and exploring new data sources while researching innovative ways to leverage both new and existing data effectively.
- Solution Research: Investigating new Machine Learning solutions to optimize every step of our value chain.
- A/B Testing: Regularly conducting and evaluating A/B tests to drive insights and refine our strategies.
You’ll be a great fit if you resonate with the following principles of Data Science and Machine Learning:
- Simplicity First: We prioritize finding the best solutions to challenges rather than forcing problems to fit predetermined solutions.
- Impact-Driven: We leverage Machine Learning to enhance our product and drive performance for our clients, always focusing on tangible business outcomes.
- Collaborative Spirit: Our teams consist of analysts, engineers, and data scientists, making strong communication skills essential for success.
- Embrace Experimentation: We believe in the power of experimentation to innovate and refine our approach continually.
Requirements
- Minimum 5 years of experience in developing data science products, from initial research through to production deployment.
- Previous experience in Ad-Tech industry is a must.
- Strong coding skills with a focus on clean, reproducible, and well-tested code.
- Proficient in key tools and technologies including Python, Spark, Hadoop, Airflow, Docker, and SQL.
- Hands-on experience with algorithms for handling sparse and large datasets, including applications in prediction, clustering, and outlier detection.
- Experience in building neural network-based products for tasks such as classification, regression, and multi-task learning is highly valuable.
- Bonus skills: Knowledge of reinforcement learning and large-scale optimization problems is a significant asset.
- Good understanding of dashboards and SQL for developing and enhancing monitoring tools.
What do we offer?
- Directly working with Founders and an opportunity to drive direct impact
- Exceptional career growth and learning opportunity
- A unique opportunity to be part of an experienced team of industry experts and entrepreneurs who bring massive change to the Adtech market
- A fun, driven, and multinational team located across Germany, India, Argentina, Canada, Spain, the UK and many more countries.
- A flexible work-from-home arrangement
- Opportunity to relocate to our office in Berlin
- 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 explain the business objective, data pipeline, feature design, offline evaluation, deployment approach, monitoring metrics, and the measurable outcome. I would also describe how I addressed reproducibility, testing, model drift, and rollback procedures.
I would first validate data quality and target definition, then explore feature engineering, regularization, calibrated sampling, and suitable categorical or embedding-based representations. I would compare offline metrics with online experiment results and monitor segment-level performance to avoid improvements that do not generalize.
I would define a clear hypothesis, primary and guardrail metrics, randomization unit, expected effect size, and statistical power before launch. I would monitor data integrity and business risk during the experiment, then assess both statistical significance and practical impact across meaningful user and campaign segments.
I use version-controlled code and configurations, automated tests, deterministic data and feature definitions, documented environments, and repeatable training pipelines. I also include model validation, monitoring, peer review, and clear experiment tracking so results can be audited and reproduced.
I would align early on interfaces, latency and throughput constraints, training-serving consistency, observability, and ownership. Together we would create a staged rollout plan with automated validation, monitoring dashboards, fallback behavior, and explicit criteria for full deployment.
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