Manager, Delivery Data Science

Curinos Inc • Toronto, ON

7d • Hybrid

About The Position

We are building the next generation of marketing personalization leveraging AI, machine learning, experimentation, and adaptive decisioning to ensure the right content reaches the right customer at the right moment. This role sits at the intersection of Delivery Data Science and Product Data Science, helping translate advanced modeling capabilities into measurable client outcomes. We are seeking a Manager, Delivery Data Science to lead the development, validation, and application of machine learning solutions powering client programs on Curinos’s proprietary marketing optimization platform. This individual will serve as a bridge between business strategy and technical execution - partnering closely with Data Science, Product, and Client Success teams to develop optimization strategies, support model governance, and drive analytical insights that improve performance. This role combines hands-on model development, experimentation strategy, and analytics leadership. You will be responsible for ensuring models are designed, validated, monitored, and communicated effectively while helping shape how personalization evolves across our client portfolio.

Requirements

• Have 5+ years of experience in Data Science, Marketing Analytics, Machine Learning, Decision Sciences, or a related field
• Have experience building, validating, and deploying predictive models in production or client-facing environments
• Possess strong statistical foundations, including experimentation, hypothesis testing, sampling methodologies, predictive modeling, and causal analysis
• Demonstrate experience supporting model governance, validation, documentation, or Model Risk Management processes
• Can effectively translate business problems into analytical and modeling solutions
• Are comfortable balancing technical depth with stakeholder communication and business impact
• Thrive in fast-paced environments with multiple priorities and evolving requirements
• Have strong presentation and storytelling skills and can communicate effectively with both technical and executive audiences
• Enjoy partnering across business, product, and technical teams to drive measurable outcomes
• Python, SQL, and data manipulation at scale
• Machine learning and statistical modeling techniques. Experience in reinforcement learning and multi-armed bandit applications is highly preferred
• Experimental design and measurement methodologies
• Model performance evaluation and monitoring
• Databricks (highly preferred), AWS, or similar cloud-based analytics environments
• Git, version control, and collaborative development workflows
• Advanced PowerPoint and data storytelling capabilities

Nice To Haves

• Experience in reinforcement learning and multi-armed bandit applications is highly preferred
• Databricks (highly preferred)

Responsibilities

• Build and Operationalize Predictive Models: Partner closely with the Data Science team to train, test, and deploy ML and AI models, within the model risk standards required by the banking industry, including model bias, disparate impact, AI guardrails, privacy controls, and ongoing monitoring. Translate business objectives into modeling frameworks, features, and optimization opportunities. Own end-to-end model development and validation including sample adequacy, out-of-time testing, and reproducibility checks before sign-off. Extend, and deploy our Reinforcement Learning-based marketing optimization capabilities while following our quality standards for model deployment and monitoring. Evaluate model performance and identify opportunities for ongoing improvement. Own model monitoring, stability tracking, and performance reporting. Help operationalize new modeling approaches within live client programs.
• Design Measurement and Experimentation Frameworks: Support the development of experimentation strategies that maximize learning and business impact. Ensure test design, audience allocation, and outcome measurement align with modeling objectives. Partner with stakeholders to define success metrics and measurement approaches. Interpret experimental results and translate findings into actionable recommendations. Identify opportunities to accelerate learning and improve future model performance.
• Deliver Advanced Analytics and Strategic Insights: Analyze customer behavior, campaign performance, and engagement signals generated by the platform. Develop analytical frameworks that uncover opportunities for growth, optimization, and improved customer experience. Deliver executive-ready insights and recommendations to both internal and external stakeholders. Translate complex analytical findings into clear business narratives. Help articulate the business value of personalization and data-driven decisioning.
• Support Team Development and Cross-Functional Collaboration: Provide technical mentorship and analytical guidance to analysts and senior analysts across the Delivery Data Science team. Establish best practices for analytics, experimentation, model evaluation, and reporting. Collaborate effectively with Product, Data Science, Client Success and Engineering teams. Act as a subject matter expert on personalization analytics and model-driven decisioning.

Benefits

• Competitive benefits, including a range of Financial, Health and Lifestyle benefits to choose from
• Flexible working options, including home working, flexible hours and part time options, depending on the role requirements
• Competitive annual leave, floating holidays, volunteering days and a day off for your birthday!
• Learning and development tools to assist with your career development
• Work with industry leading Subject Matter Experts and specialist products
• Regular social events and networking opportunities
• Collaborative, supportive culture, including an active DE&I program
• Employee Assistance Program which provides expert third-party advice on wellbeing, relationships, legal and financial matters, as well as access to counselling services

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed