Senior Data Scientist - Insurance

As a Senior Data Scientist at Tiger Analytics, you will engage in a diverse range of responsibilities, such as collaborating with business partners to devise innovative analytical solutions, leading analytic approaches, integrating solutions into applications with data engineers, and advocating for data-driven decision making within the company.

Tiger Analytics requires candidates for the Senior Data Scientist role to hold a Bachelor's Degree in Data Science, Computer Science, or a related field and possess over five years of experience in Data Science and Machine Learning, particularly within the Insurance domain, with strong skills in analytical modeling techniques.

As a Senior Data Scientist at Tiger Analytics, you’ll be part of a fast-growing team which emphasizes significant career development through responsibilities that enhance your skills, collaborative opportunities with experienced professionals, and participation in conferences that expand your knowledge and networks.

The role of a Senior Data Scientist is crucial at Tiger Analytics because it directly influences the success of our clients by delivering high-value data-driven insights, while also contributing to the overall strategy and performance of the company through innovative analytics solutions.

Candidates applying for the Senior Data Scientist position at Tiger Analytics are encouraged to have experience with data visualization tools such as Tableau and R Shiny, along with a solid foundation in machine learning concepts and techniques to effectively solve complex analytical problems.

When answering this question, focus on specific machine learning projects you’ve completed in the insurance sector. Highlight the models used, the business problems addressed, and the positive outcomes achieved, showcasing your proficiency in techniques like regression, classification, and clustering.

Discuss your method for identifying and creating meaningful features from large datasets. Emphasize your analytical thinking, how you leverage domain expertise, and any tools you employ to manage and derive insights from the data effectively.

Highlight your ability to simplify complex data insights, using clear visuals and relatable language. Cite a specific example where your communication resulted in strategic decision-making, reinforcing the importance of your analytical skills in business contexts.

Explain your approach to model design, emphasizing best practices such as modular coding, robust validation processes, and comprehensive documentation. Mention how these strategies enhance the usability and adaptability of data products.

Discuss your time management techniques, such as using prioritization matrices or agile methods. Emphasize your bias for action, how you tackle urgent tasks while still meeting long-term goals effectively.

Share your experiences working with cross-functional teams, detailing how you facilitated collaboration between data engineers, business leads, and other stakeholders to deliver comprehensive analytical solutions.

Mention your commitment to continuous learning through attending conferences, participating in workshops, subscribing to industry publications, and engaging with the data science community, ensuring you stay at the forefront of industry knowledge.

Discuss the various performance metrics you use to assess models, such as accuracy, precision, recall, or F1 scores, and how you interpret these metrics to determine the model's effectiveness in meeting business needs.

Use this question to highlight your problem-solving skills. Talk about the specific challenges faced in a data project, the strategies you employed to tackle those challenges, and the successful outcomes achieved.

Articulate the transformative impact of data-driven decision-making on business strategies and performance, highlighting how it leads to better customer insights, tailored marketing strategies, and improved operational efficiencies.