Data Scientist II

As a Data Scientist II at Oscar, you'll play a crucial role in driving our data initiatives forward. Your responsibilities will include collaborating with data and business peers, mentoring junior data scientists, and developing robust data pipelines and statistical models. You'll also have the opportunity to deliver analytical solutions with minimal oversight, ensuring alignment with departmental strategies.

To be a successful candidate for the Data Scientist II position at Oscar, you should have at least 3 years of experience in a quantitative field, with 2 years focusing on SQL, R, or Python for data manipulation. Experience with building data models and advanced analytics is essential, and a master's degree or knowledge of the health insurance sector would be highly beneficial.

The Data Scientist II role at Oscar offers flexible remote work options. If you live in a state within our specified list, you can work from home. However, if you live within commutable distance to one of our offices, you are expected to come into the office at least two days each week, fostering a hybrid work culture.

Oscar offers a competitive base pay for the Data Scientist II role, which ranges from $131,200 to $172,200 per year. In addition to the base salary, employees are eligible for benefits, company equity grants, and annual performance bonuses, making it a rewarding opportunity.

Oscar prides itself on a vibrant and inclusive company culture where every employee is valued. As a Data Scientist II, you'll find a supportive environment that encourages authenticity and collaboration. We believe in fostering a workplace where diverse backgrounds and perspectives thrive and contribute to transforming healthcare experiences.

When answering this question, highlight specific methodologies you’re familiar with, such as regression analysis, clustering, or predictive modeling. Explain how you applied these techniques in past projects, focusing on the goals and outcomes to showcase your practical experience.

Share a relevant project where you effectively applied machine learning algorithms. Specifically, highlight the problem you were solving, the algorithms you chose, the results achieved, and any lessons learned during the process. This will demonstrate your technical capability and problem-solving skills.

Discuss your approach to maintaining data quality, such as implementing data validation checks, cleaning datasets, and performing exploratory data analysis. Provide examples of how your strategies have positively impacted your analyses in previous positions, which illustrates your attention to detail.

Outline your proficiency level in SQL, R, and Python, emphasizing the specific tasks you've accomplished using these tools. Give examples of past projects where you utilized each, demonstrating your versatility and technical skills in data manipulation and analysis.

Illustrate your ability to translate complex data findings into understandable insights for non-technical stakeholders. Mention specific instances where you've successfully communicated data insights, fostering understanding and collaboration, showcasing your ability to bridge the gap between technical and non-technical domains.

Describe your collaborative working style, emphasizing communication, sharing of ideas, and how you value input from your teammates. Discuss an example of a successful team project where your collaboration led to outstanding results, reinforcing your team-oriented mindset.

List advanced analytics methods you have utilized, such as time series analysis, text analytics, or deep learning. Be prepared to explain how you have applied these methods in real-world scenarios, demonstrating their relevance and effectiveness in tackling complex data challenges.

Share a specific scenario where you encountered a data problem, detailing your troubleshooting process. Emphasize your analytical thinking and problem-solving skills by discussing the steps you took to identify and resolve the issue, ensuring minimal disruption to the project.

Explain your passion for data science and how it aligns with Oscar's mission. Discuss aspects such as solving real-world problems, making data-driven decisions, or improving user experiences, which reflect your dedication to positively impacting health insurance through data.

Share the resources you use to stay informed about trends and innovations in data science, such as attending conferences, participating in online forums, or reading industry publications. Highlight your proactive approach to continuous learning, which is essential in the ever-evolving field of data science.