Economist / Applied Scientist, Media Optimization

The Economist / Applied Scientist at Haus will focus on research and development, building innovative causal marketing measurement tools. Responsibilities include partnering with engineering teams to deploy causal models, leveraging existing literature, advocating for rigorous scientific solutions, and supporting customers post-launch. This position plays a pivotal role in impacting business decisions for our clients.

To qualify for the Economist / Applied Scientist role at Haus, candidates should possess an MSc or PhD in Economics or equivalent experience, alongside at least 2 years in an economist or data science role. Additionally, expertise in Python and SQL, as well as familiarity with incremental testing, MMM, and causal inference methodologies, is essential. A strong problem-solving mindset and the ability to communicate technical concepts effectively are also critical.

At Haus, we believe in the power of experimentation to refine marketing strategies. Our decision science platform utilizes causal inference models to run experiments that quantify the impact of marketing actions on the bottom line. This approach also helps us to formulate solutions tailored to specific customer pain points, ensuring brands can make informed decisions.

Haus collaborates with a range of well-known brands, such as FanDuel, Sonos, and Hims & Hers. These partnerships are designed to help these organizations leverage our innovative decision science platform to achieve significant returns on marketing investments while navigating the complexities of the digital privacy landscape.

At Haus, you’ll find a collaborative and innovative work environment where creativity is encouraged. The team comprises experts from diverse backgrounds, fostering an atmosphere of shared responsibility and open communication. We embrace ambiguity, inviting team members to take exploratory steps towards breakthrough solutions while aligning with our vision for growth and success.

In answering this question, you should highlight specific projects where you utilized causal inference models in marketing contexts. Discuss the methodologies applied and the results achieved, showing how your work influenced decision-making. Be prepared to mix technical language with layman’s explanations to demonstrate your adaptability.

To tackle this question, focus on past collaborative efforts with engineering teams, underscoring your role in bridging technical and scientific knowledge. Discuss specific challenges faced, how you communicated with team members, and the impact of your collaboration on model deployment and overall product success.

Highlight your proficiency in essential tools for this role, especially Python and SQL. Provide examples of how you've used these languages in your previous work to analyze data, build models, or perform experiments. This shows not just familiarity but the depth of experience that is valuable for Haus’s projects.

When answering this, recount a situation where you successfully presented a scientific solution to stakeholders, detailing your approach to advocacy and the significance of your recommendations. Highlight how you balanced scientific integrity with business needs to achieve buy-in from your audience.

In your response, discuss the methods you use to stay informed about industry trends, such as reading academic journals, joining professional organizations, or attending conferences. This demonstrates your commitment to ongoing learning and adaptation in the fast-evolving field of marketing measurement.

Explain your approach to embracing ambiguity, emphasizing how you break down complex problems into manageable components. Provide examples of past experiences where you structured unstructured problems, demonstrating your analytical skills and innovative thinking.

Share your methods for effective communication, emphasizing clarity and relatability. Discuss the importance of tailoring your message to the audience, using visuals or analogies, and fostering an interactive discussion to ensure understanding.

Detail a specific project involving incremental testing, explaining your role and the metrics that were analyzed. Highlight how the results informed strategic decisions for the business and the overall impact it had, showcasing your analytical and application skills.

Articulate your enthusiasm for the role by connecting your personal and professional values with Haus's mission. Highlight how the opportunity to contribute to innovative decision science tools in a collaborative environment aligns with your career aspirations and passion for driving impactful change.

Discuss your methods for effective prioritization, mentioning tools or systems you use to manage deadlines and project demands. Highlight the importance of communication with stakeholders to ensure all aspects of each project are executed smoothly and efficiently.