Senior Marketing Data Scientist (m/w/d)

As a Senior Marketing Data Scientist at our client, your main responsibilities will revolve around developing and leading structured testing strategies for paid media. You will design causal inference methodologies like A/B tests and time-based experiments, analyze experiment results, and translate data-driven insights into actionable recommendations to optimize media spending. Collaborating with marketing teams, you will also create a knowledge hub for past experiments and educate stakeholders on measurement techniques, ensuring robust methodologies are applied across the board.

To apply for the Senior Marketing Data Scientist role at our client, you should have at least 5 years of experience in marketing analytics or data science, particularly within the digital marketing field. Knowledge of A/B testing, causal inference, and advanced measurement techniques is crucial. Strong skills in SQL, Python, R, or similar analytics tools are preferred, alongside excellent communication abilities to present complex data as clear business insights to senior stakeholders.

The Senior Marketing Data Scientist contributes to decision-making at our client by analyzing experiment results to determine statistical significance and providing insights on incremental impacts. By developing frameworks for benchmarking and meta-analysis, you will effectively communicate data-driven recommendations that will influence budget allocations and investment strategies, thereby guiding marketing efforts towards maximizing effectiveness and efficiency.

In the role of Senior Marketing Data Scientist at our client, you can expect numerous development opportunities. You will stay ahead of emerging trends in marketing measurement, allowing you to evolve your skills. Additionally, by leading innovative experiments and collaborating with cross-functional teams, you will experience professional growth that enhances your expertise in marketing analytics and data science.

The work-life balance for a Senior Marketing Data Scientist at our client is designed to promote flexibility and well-being. The company offers a hybrid working model that allows for 60% or more remote work each week, alongside additional benefits such as 27 vacation days and mental health support. This approach ensures that you maintain a healthy balance between professional and personal life while contributing significantly to the company's goals.

A/B testing involves comparing two versions of a marketing element - such as an ad or landing page - to determine which performs better. It's significant because it helps identify which approach yields higher engagement, conversions, or other defined outcomes, enabling data-driven decision-making in marketing strategies.

To ensure statistical significance in experiments, I carefully plan sample sizes and make use of appropriate statistical tests. By establishing clear metrics and thresholds beforehand, I can assess the results with confidence, ensuring that our findings are reliable and actionable.

For causal inference, I use methodologies such as regression modeling, propensity score matching, and time-series analysis. These techniques allow me to determine the impact of marketing interventions while controlling for confounding variables, providing more accurate insights into effectiveness.

When communicating complex data insights, I focus on tailoring my message to the audience. I simplify the concepts using visuals and relatable examples, and I emphasize the business implications of the data, ensuring stakeholders can grasp the value and make informed decisions.

One challenge in marketing measurement has been attribute discrepancies across platforms. To overcome this, I implemented standardized measurement frameworks and cross-channel benchmarks to align our reporting system, ensuring consistency and clarity in the results.

In a recent project, I led a comprehensive A/B testing initiative across multiple digital channels. By analyzing the results, we optimized our media spend, which significantly increased our ROI. The collaboration with other teams also fostered knowledge sharing, enhancing our overall marketing strategies.

I am proficient in using SQL for database querying, Python and R for data manipulation and statistical analysis, and Tableau for data visualization. These tools have equipped me to handle large datasets effectively and derive insights that guide marketing decisions.

I stay updated through continuous learning via industry blogs, webinars, and professional networks. Attending conferences and participating in relevant courses also helps me adapt to new methodologies and technologies that can enhance our marketing measurement efforts.

To prioritize testing initiatives, I align them with strategic marketing objectives and assess their potential impact. I consider factors such as budget, resources, and existing performance metrics to create a roadmap that maximizes insights while supporting long-term goals.

Teamwork is crucial in my approach as it fosters collaboration and diverse perspectives. By working closely with marketing and analytics teams, we can leverage each other's strengths, share insights, and create more robust solutions that drive impactful results in our campaigns.