Lead Data Scientist - Healthcare Claims Data

As a Lead Data Scientist at Tiger Analytics, your primary responsibilities will include developing and executing product strategies for healthcare claims data initiatives, leading a cross-functional team, designing quarterly plans to meet KPIs and business objectives, and collaborating with internal stakeholders to translate requirements into actionable plans. You will also mentor junior team members and stay engaged with industry trends through conferences.

To qualify for the Lead Data Scientist role at Tiger Analytics, candidates should possess a graduate degree in Data Science, Computer Science, or a related field. You’ll need at least 8 years of Data Science experience with a focus on healthcare claims data, along with 5 years of experience in product management. Leadership experience and strong analytical skills are also essential.

In this role, you'll lead a cross-functional team of 5-6 talented Data Scientists. You'll be responsible for nurturing a high-performance environment while fostering collaboration and communication. Leading by example, you’ll mentor your team and encourage them to engage in continuous learning and professional growth.

Tiger Analytics values continuous learning and professional development for Lead Data Scientists. You'll have opportunities for growth through mentoring of team members, attending industry conferences, and applying the latest techniques in data science. The entrepreneurial environment encourages innovating solutions and collaboration within the team.

The ideal candidate for the Lead Data Scientist position at Tiger Analytics will demonstrate exceptional analytical skills with a strong focus on healthcare data analysis. Leadership abilities, effective communication, and collaboration skills are crucial, as well as a proactive and flexible mindset in task prioritization and time management.

When answering this question, it’s important to focus on specific projects or roles you've held involving healthcare claims data. Highlight your responsibilities, the analytical tools used, and how your contributions positively impacted outcomes or efficiencies within those projects.

In your response, emphasize your leadership style and how you prioritize collaboration. Discuss techniques you use to foster communication and unite different team members toward common goals, along with examples where you've successfully led a team in the past.

Talk about the planning processes you implement, including how you set and communicate clear targets, and the tools you use to regularly track progress. Mention any frameworks you've applied that integrate feedback loops to keep the project on track.

Provide a specific example of a data science challenge you faced, detailing the problem, the approach you took to resolve it, and the results achieved. Highlight any teamwork, innovative solutions, and data-driven decision-making in the process.

Discuss how you regularly attend conferences, engage with the data science community, read industry publications, and participate in continuous learning opportunities to stay aware of emerging trends and technologies in data science.

Illustrate your ability to simplify complex concepts by providing a specific scenario. Discuss how you tailored your communication style to suit your audience and ensured they understood the implications of the data insights.

List the data analysis tools and programming languages you are proficient in, such as Python, R, or specific data visualization software. Explain the contexts in which you have applied these tools to deliver insights effectively.

Explain your prioritization methodology, including how you assess project urgency and alignment with business goals. Use examples of techniques like time management or agile methodologies to illustrate your approach.

Discuss key metrics you believe are essential for evaluating healthcare claims data, such as claim approval rates, processing times, or fraud detection metrics. Explain why these metrics matter in the broader context of healthcare analytics.

Share your strategies for promoting a collaborative working environment, such as encouraging open communication, facilitating brainstorming sessions, and recognizing contributions from team members to build a culture of trust and support.