Director of Data Science

Key Responsibilities

• 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

• 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

• 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.