Senior Data Scientist - Pricing

As a Senior Data Scientist - Pricing at Lime, your primary responsibilities will include developing and optimizing pricing models that balance revenue growth and customer retention. You will also assess price elasticity and demand patterns while analyzing data from A/B tests to measure the effectiveness of pricing strategies. Collaborating with teams across various departments, you'll provide insights that help shape strategic decisions and enhance customer engagement.

To succeed as a Senior Data Scientist - Pricing at Lime, candidates should possess an MS or PhD in Statistics, Economics, or a related field along with at least 5 years of industry experience focusing on pricing and revenue optimization. A deep understanding of probability and statistics, proficiency in SQL and Python, and experience in machine learning and predictive modeling are essential for this role.

The Senior Data Scientist - Pricing plays a critical role in contributing to business growth at Lime by developing pricing strategies that maximize profitability while enhancing customer retention. By analyzing data and providing strategic insights, this role helps ensure that pricing decisions align with market demands and competitive positioning, ultimately driving revenue and customer satisfaction.

Key skills for a successful Senior Data Scientist - Pricing at Lime include a solid foundation in statistics and machine learning, proficiency in SQL and Python for data manipulation, and strong business acumen to understand pricing strategies’ impact on customer behavior. Additionally, excellent communication skills are a must for translating technical insights into actionable recommendations for non-technical stakeholders.

Lime stands out as a unique workplace for a Senior Data Scientist - Pricing due to its commitment to sustainability and innovation in the micromobility sector. As part of a diverse team dedicated to making a positive impact on urban transportation, you’ll work on high-stakes projects that influence both business strategy and customer engagement while contributing to a greener future.

When answering this question, share specific examples of pricing models you’ve developed, the methodologies you used, and how those models have impacted business outcomes, aligning them with Lime's mission.

Discuss your preferred methods for price elasticity estimation, such as regression analysis or machine learning techniques, and explain why you find them effective in the context of pricing strategies.

Describe your process for analyzing A/B test results, emphasizing how you assess statistical significance, interpret data, and communicate findings to influence pricing decisions at Lime.

Share specific challenges you've encountered, such as data limitations or stakeholder disagreements, and detail the strategies you've implemented to overcome those challenges.

Explain your approach to transforming analytical results into actionable recommendations, including how you prioritize insights and communicate them effectively to cross-functional teams.

List the tools you are proficient in for data analysis and visualization, such as SQL, Python, or Tableau, and provide examples of how you’ve used them in previous roles.

Share a specific scenario where your pricing strategy led to enhanced customer retention, detailing the metrics you tracked and the outcomes of your implementation.

Discuss your methods for keeping up-to-date with pricing trends, such as reading industry reports, attending webinars, or participating in professional networks, and explain how these trends influence your work.

Provide an example of a project where you applied machine learning techniques in pricing, describing the tools used, the results achieved, and any lessons learned from the process.

Describe your conflict resolution approach when facing disagreements about pricing strategies, emphasizing collaboration, data-driven discussions, and finding common ground.