Lead Data Scientist
| Company | Goosehead Insurance |
|---|---|
| Location | Lakewood, CO, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | |
| Experience Level | Senior |
Requirements
- Strong programming skills in Python, with experience utilizing libraries such as Pandas, scikit-learn, or similar.
- Proficient in SQL and working with large-scale relational databases.
- Experience with data visualization tools (e.g., Tableau or equivalent).
- Strong storytelling skills and the ability to explain complex concepts to non-technical stakeholders.
- Demonstrated experience leading end-to-end data science projects with measurable business outcomes.
Responsibilities
- Design, build, and deploy predictive models to improve decision-making in areas such as service staffing optimization, marketing attribution, demand forecasting, and client retention.
- Analyze complex datasets to identify patterns, generate insights, and support business strategy.
- Partner with business stakeholders to frame problems, prioritize use cases, and measure the impact of data science initiatives.
- Work closely with data engineering to shape data infrastructure and pipelines to support scalable model development.
- Create compelling visualizations and reports to communicate findings clearly to technical and non-technical audiences.
- Contribute to the development of best practices in data science, including model governance, code review, documentation, and reproducibility.
- Stay current with trends in machine learning, AI, and data tooling and bring innovative thinking to the team.
- Follow company policies and procedures related to data security and privacy.
- Participate in ongoing training for data handling best practices.
- Treat client and company data with the highest standards of confidentiality.
- Report any security or privacy incidents promptly.
- Provide technical guidance and mentorship to junior team members.
Preferred Qualifications
- Familiarity with cloud platforms (e.g., AWS, Azure) and distributed computing (e.g., Spark).
- Understanding of design, deployment, and testing of custom LLMs.
- Experience deploying machine learning models to production environments.
- Background in insurance, financial services, or other highly regulated industries.
- Experience with A/B testing or causal inference methods.