Director of Data Science

Data Science Director

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.