Senior Data Scientist - Pricing Optimisation

To be a strong candidate for the Senior Data Scientist - Pricing Optimisation position at Fever, you'll need solid foundations in math, statistics, and machine learning, typically evidenced by a master's or PhD degree. A deep understanding of statistical analysis, modeling, and tools such as SQL and Python libraries like Pandas and Numpy is also crucial. Prior experience in developing and deploying models for business applications will set you apart!

As a Senior Data Scientist - Pricing Optimisation at Fever, your primary responsibilities will include collaborating with business, product, and engineering teams to create data-driven solutions that enhance pricing strategies. You'll be responsible for developing models to estimate demand and price elasticity, designing and analyzing experiments, and identifying cross-effects among products to optimize pricing. This role is instrumental in advancing Fever’s data culture!

The work culture at Fever for a Senior Data Scientist - Pricing Optimisation is dynamic and collaborative. Our team emphasizes transparency, inclusivity, and ongoing professional development. You will be part of a tight-knit group that prioritizes knowledge sharing and learning from one another. There is plenty of room for creativity and innovation, and we encourage you to bring your unique insights to the table!

In your first few months as a Senior Data Scientist - Pricing Optimisation at Fever, you’ll integrate into the team, collaborate with key partners, and familiarize yourself with our data assets and technology stack. Expect to get involved in meaningful Data Science projects right away and actively participate in team events, helping you settle into your role smoothly and effectively.

Fever offers an attractive compensation package for the Senior Data Scientist - Pricing Optimisation role, including a competitive base salary, stock options, and performance-related bonuses. You’ll enjoy a flexible hybrid working model, health insurance, discounts on Fever events, and unique perks like English lessons and a Gympass membership. Work-life balance is prioritized, ensuring you thrive both personally and professionally.

In answering this question, share specific projects where you developed pricing models, discussing the methods you used, such as regression analysis or econometrics. Highlight how you adapted these models based on business needs, and discuss how your approach aligns with Fever’s goal of optimizing event pricing.

To answer this, talk about validating your models through cross-validation, A/B testing, and performance metrics. Emphasize the importance of not just focusing on accuracy but also on understanding how to evaluate failure modes. This shows that you consider multiple aspects of model performance, which is crucial for a role at Fever.

Provide a concrete example where your insights led to a successfully implemented optimization, detailing your analytical approach, the data you relied on, and how you communicated your findings to stakeholders. This response should illustrate your problem-solving skills and strategic thinking.

Highlight that effective communication is essential in your role to translate complex data insights into actionable recommendations for diverse stakeholders. Provide examples of how you’ve previously tailored your communication for technical and non-technical audiences to ensure clarity and collaboration.

Discuss your commitment to continuous learning through resources like online courses, webinars, industry publications, coding challenges, and participation in relevant communities. Mention specific platforms you use to keep your skills sharp and knowledge up-to-date.

In your response, cover your familiarity with various machine learning algorithms, specifying a few you've implemented in practice. Explain the projects you've worked on, the goals of those projects, and the outcomes, showcasing how your experience aligns with Fever’s data-driven objectives.

Anticipating challenges shows foresight; mention complexities in accurately capturing consumer behavior or navigating data silos. Discuss your strategies for addressing these challenges, like iterative model refinement or cross-functional teamwork, emphasizing your proactive approach.

Explain that prioritization is based on aligning tasks with business impact and deadlines. Discuss methods you use, such as the Eisenhower matrix or Agile methodologies, to organize workload efficiently while ensuring that urgent tasks don’t compromise quality.

Talk about the data visualization tools you've used, such as Tableau or Matplotlib, emphasizing how you leverage visual storytelling to convey insights to stakeholders. Detail specific instances where visualizations played a key role in decision-making, showcasing this essential skill in the Fever context.

Discuss trends like the increased focus on ethical AI and reproducibility, advancements in automated machine learning, or innovative approaches to causal inference. Sharing insights on these emerging trends will demonstrate your forward-thinking approach and understanding of the evolving data science landscape.