Director, Data Science at Gore Mutual Insurance in Cambridge, Ontario, Canada; Toronto, Ontario, Canada

Job description

• This leadership position requires a unique combination of technical expertise, strategic thinking, and innovation to leverage data and advanced analytics solutions that drive business growth, enhance operational efficiency, and ensure regulatory compliance. This role will lead a team of data scientists focused on delivering measured value through AI/ML use cases that span across all aspects of Gore. You will lead and mentor a team of high performing data scientists and shape our growth strategy through empirical studies and experimentation. This role is resourceful, analytically rigorous, with extensive experience leading data science teams and a passion for solving product problems.
• This role is a mix of consulting know-how (problem solving, thought leadership, communication), analytical proficiency in statistics, data science and machine learning, agentic workflows, and, when required, hands-on proficiency in SQL/Python programming, visualization methods and technologies, and data engineering / infrastructure.
• This role is responsible for leveraging data science to assess risks, improve underwriting, detect fraud, and optimize pricing models while unlocking valuable insights, optimize processes, and inform decision-making. By implementing advanced analytics and ensuring data integrity, this role enables the organization to make informed decisions, streamline operations, and ultimately provide better coverage and services to policyholders while managing risks effectively.

Responsibilities

• Model Development & Deployment
• Managing a data science project from end-to-end, including collaborating with partners and stakeholders to understand the business problem, obtaining data, defining an experimental design, setting up a model building pipeline, overseeing a team of data scientists to build necessary models, and working with a team of developers to implement models and managing a monitoring process
• Developing and maintaining predictive models using advanced ML/AI techniques. Building Supervised and unsupervised learning models LLM workflows and agents.
• overseeing the development of analytical products and machine learning models, AI models, LLM workflows and agents, building and enabling analytics infrastructure including production model deployment, as well as Non Prod experimentation, deployment and testing
• Define metrics to measure the impact of AI initiatives on business outcomes.
• Model Maintenance, Governance & Validation
• Design detailed validation plans and perform quantitative, conceptual and technical assessment of models; Discuss and effectively communicate validation observations and findings with teams.
• Accountable for the integration of AI solutions, application deployment in production including, app health, resiliency, performance, security, enterprise data management standards, ethical and privacy standards
• Continuously monitor and improve the performance of AI models, ensuring their accuracy, reliability, scalability, and ethical application. Includes optimization of our end-to-end machine learning pipelines, scaling, automating, and monitoring our predictive models and pipelines.
• Strategic direction on compliance for the design, development, and implementation of AI models into operational workflows, including helping draft and review the procedures that support a compliance system. Responsibilities include guiding AI teams with project planning, system architecture, risk management, verification & validation, and continuous monitoring.
• Leadership & Cross-Functional Collaboration
• Lead a team of data scientists to develop, plan, and execute multiple analytical projects creating and delivering high quality AI and analytical solutions across a broad spectrum of projects and business lines.
• Being a change champion and help the business conduct business problem opportunities and assessment identifying revenue generation and cost saving opportunities, developing proposals and make recommendations for model usage to the downstream partners.
• Advocates and advances modern, Agile solution delivery practices, great design, engineering and organizational practices, challenging the team to utilize creative thinking to modify or select the most suitable procedure/approach to solve a business problem balancing complexity and value.
• Driving change within team and across broader Data & Analytics the importance of learning our business and the respective to help aid in our building the best solutions – be a business expert.
• Guide project teams in synthesizing analytical findings for consumption by internal analytical clients and business executives.Excellent communicator who is able to convey complex information in an understandable, compelling, and persuasive manner to non-technical clients and executives and junior data scientists
• Strategy
• Creates the advanced analytics strategy, ensuring technology solutions comply with enterprise AI/ML standards, model governance and model risk standards, and AI Ethics, Fairness and Bias related control practices
• Conceptualize, design and execute an ambitious data science, ML and AI (e.g., LLM and agentic) roadmap
• Own the business outcomes, KPIs and deliverables of that roadmap working across stakeholders
• Identify and define new strategic ML opportunities and work with cross functional teams to understand business requirements and guide the team to provide data-driven solutions, improving how we do data collection, storage, experimentation and analysis.
• Research & Innovation
• Stay current with the latest advancements in Agentic/AI/ML solutions and evaluate their applicability to Gore
• Develop strategic relationships and partnerships with the startup, academia, and industry ecosystem to garner mindshare and learnings.
• Foster a culture of innovation by encouraging research, experimentation and exploration of novel AI techniques

Requirements

• Minimum Master’s degree, Ph.D. Highly preferred in a technology/analytical field such as Computer Science, Physics, Economics, Data Science, Operations Research, Machine Learning, Engineering, or other relevant scientific fields
• Over 10 years of expereince with a minimum 8 years of relevant professional experience, 4+ years of leading a team in developing and implementing AI/ML solutions (predictive, prescriptive, ML etc.)with an excellent understanding of the underlying Statistical, Machine Learning theory, and Predictive Modeling Lifecycle
• Extensive experience in building, deploying, and managing production-ready generative models and machine learning models.
• Foundational and applied knowledge of statistical analyses, data science, and machine learning including, but not limited to descriptive, inferential, or causal statistics, predictive and forecast modeling, optimization modeling, NLP, LLM and agentic workflows and A/B testing methods
• Deep expertise in developing and maintaining predictive models using advanced ML/AI techniques. Proficiency in building Supervised learning models [ (classification and regression) – tree-based (Random Forest, Stochastic Gradient Boosting and eXtreme Gradient Boosting, Decision Trees, Extra Trees, Regularized Greedy Forests), Generalized Linear Models, Discriminant models (LDA, MDA, FDA and QDA)], and Unsupervised learning models (Isolation Forest, Clustering algorithms), Reinforcement learning, LLMs, agentic systems, graph and network analysis
• Deep knowledge and hands-on experience in Machine Learning, Data Science, and AgenticAI, including experience with MLOps to build end-to-end pipelines and deploy models in production
• Experience in building Deep neural networks (MLP, CNN, RNN, GRUs, and LSTMs) and use of AI/Deep Learning frameworks like TensorFlow, PyTorch, CNTK, and Keras
• Experience with cloud platforms such as AWS, Google Cloud, or Azure.
• Proficiency in SQL, Python, Pandas, Numpy, Scikit-learn, TensorFlow, PyTorch etc.
• Excellent problem-solving skills, strategic thinking, and a proactive approach to challenges.
• Exceptional communication skills, with the ability to explain complex concepts to a non-technical audience and influence top-tier executives. Proven ability to translate complex data findings into actionable business strategies and drive business growth
• Deep understanding of the complexities and tradeoffs of leveraging/deploying ML/AI at scale
• An ideal candidate will have a passion for AI/ML and to foster a team of AI/ML and business experts and increase the data and analytics literacy with the business. They have an analytical mindset, be a self-starter, comfortable with ambiguity, detail oriented and have desire to work in a fast-paced, cross-functional environment while influencing business performance through insights.

Skills

Required

• SQL
• Python
• Pandas
• Numpy
• Scikit-learn
• TensorFlow
• PyTorch
• MLOps
• Machine Learning
• Data Science
• AgenticAI
• Statistical analysis