What are the responsibilities and job description for the Director of Data Science position at Goosehead Insurance Agency LLC?
Job Details
- Key Responsibilities
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- Set the Vision: Define the strategic direction for data science across Goosehead, ensuring alignment with enterprise goals and long-term AI ambitions.
- Leadership and Team Development: Build, mentor, and scale a high-performing team of data scientists while fostering a culture of technical rigor, creativity, and impact.
- Strategic Partnership: Act as a trusted advisor to senior executives and business leaders, translating complex challenges into actionable AI strategies.
- Applied Machine Learning: Oversee the design, development, and deployment of ML systems driving automation, personalization, and operational efficiency.
- Methodology and Innovation: Advance the organization's analytical frameworks, experimentation design, and causal inference capabilities.
- Operational Excellence: Establish best practices for MLOps, model lifecycle management, governance, and responsible AI.
- Collaboration with Engineering: Partner with the Director of AI Infrastructure and Data Architecture to ensure seamless integration between data science models and underlying data and ML platforms.
- Measurement and Storytelling: Define success metrics for AI initiatives and communicate outcomes clearly to executive and non-technical audiences.
- Technology Leadership: Stay at the forefront of AI and ML research, bringing emerging technologies such as LLMs, generative AI, and reinforcement learning into practical business use cases.
- Experience and Qualifications
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- 10 years of experience in data science or machine learning, including at least 3 years in a leadership capacity managing teams and enterprise-level initiatives.
- Proven track record of developing and deploying ML models that drive measurable business value.
- Deep proficiency in Python and modern ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch, with strong SQL skills.
- Expertise in model development, feature engineering, and experimentation across structured and unstructured data.
- Strong understanding of MLOps principles, CI/CD for ML, and cloud-native architectures such as Azure, Databricks, and Snowflake.
- Experience designing and operationalizing responsible AI frameworks, including governance, fairness, and interpretability.
- Exceptional ability to communicate complex technical concepts to senior stakeholders in clear, business-relevant terms.
- Demonstrated success building cross-functional relationships across engineering, analytics, and business domains.
- Preferred Qualifications
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- Advanced degree (MS or PhD) in Computer Science, Statistics, Applied Mathematics, or a related quantitative field.
- Experience with large-scale experimentation, causal inference, or reinforcement learning.
- Familiarity with LLMs, RAG pipelines, and prompt engineering for applied business use.
- Background in insurance, financial services, or other regulated industries.
- Experience building and scaling AI functions or centers of excellence within a growing enterprise.
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