Lead Data Scientist

Job description

**This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Data Scientist based in Canada.** As a Lead Data Scientist, you will drive end-to-end data science initiatives that transform complex datasets into strategic insights and automated solutions with measurable business impact. You will design, deploy, and scale advanced statistical and machine learning models across critical areas, including credit risk, pricing, collections, and fraud prevention. Working at the intersection of technical leadership and business strategy, you will collaborate with cross-functional teams to integrate analytical solutions into production systems and improve decision-making. You will help establish best practices for model development, code quality, documentation, and ongoing performance monitoring. In addition to leading complex projects, you will mentor fellow data scientists and promote a culture of continuous learning and analytical rigor. This remote-first opportunity offers the chance to apply advanced technology to expand access to financing for small businesses while contributing to a collaborative, impact-driven environment. ### Accountabilities - Develop advanced machine learning models: Design, build, and deploy statistical and machine learning models for credit risk, pricing, collections, fraud detection, and other high-impact business applications. - Lead end-to-end data science initiatives: Own the complete lifecycle of analytical projects, from problem definition and model design through deployment, monitoring, maintenance, and continuous improvement. - Drive cross-functional collaboration: Partner with Portfolio Strategy, Engineering, Product, Underwriting, Sales, and Collections teams to integrate models into applications and address critical business challenges. - Establish technical standards: Define best practices for model development, code quality, technical documentation, and implementation, while guiding design decisions across the team. - Build production-ready ML infrastructure: Develop and maintain scalable machine learning pipelines, automated workflows, and monitoring systems to ensure model reliability and performance. - Mentor and support team members: Provide technical guidance, share expertise, and foster a culture of continuous learning, collaboration, and rigorous analytical thinking. - Communicate insights effectively: Translate complex technical concepts, model outcomes, and business implications into clear recommendations for technical and non-technical stakeholders. - Improve business decision-making: Identify opportunities to apply data science and automation to strengthen operational efficiency, risk management, and business performance. ## Requirements: - Professional experience: 8+ years of hands-on experience developing and deploying models using advanced statistical and machine learning techniques. - Machine learning expertise: Strong experience with generalized linear models, gradient boosting, deep learning, and other relevant predictive modeling approaches. - Credit risk modeling: Deep experience building and deploying credit risk models, particularly underwriting models, in fintech, lending, or financial services is highly preferred. - Production machine learning: Experience with production-grade ML pipelines, real-time models, and automated decisioning engines is preferred. - Programming proficiency: Expert-level knowledge of Python, SQL, and Git. - Cloud platforms: Experience deploying and managing models in cloud environments, particularly AWS and Amazon SageMaker. - Statistical foundations: Strong understanding of statistics, machine learning principles, and experimental design. - Workflow orchestration: Familiarity with workflow orchestration tools such as Metaflow is preferred. - Data infrastructure and specialized tools: Experience with cloud data warehouses such as Snowflake or Databricks, model monitoring platforms such as Arize, decision engines such as Taktile, and feature stores such as Tecton is a plus. - Education: Bachelor's degree in Financial or Applied Mathematics, Operations Research, Economics, Statistics, or a related field. A master's degree or PhD is an advantage. - Project management and communication: Strong organizational skills and the ability to lead complex initiatives, coordinate stakeholders, and communicate technical findings effectively. - Critical thinking and problem-solving: Ability to evaluate complex challenges, assess trade-offs, and develop robust, practical solutions. - Leadership and collaboration: Demonstrated ability to guide technical decisions, mentor colleagues, and work effectively across multidisciplinary teams. ## Benefits: - Competitive base salary: Annual compensation ranging from CA$172,000 to CA$237,000, depending on qualifications, experience, skills, and internal equity. - Annual performance bonus: Opportunity to earn an additional 12% annual bonus. - Comprehensive healthcare: Medical, dental, and vision coverage. - Flexible time off: Flexible paid time-off policy. - Paid parental leave: Support for employees and their families. - Retirement savings: RRSP matching contributions. - Wellness support: Wellness reimbursement to support personal well-being. - Professional development: Annual budget for learning and career development. - Community involvement: Paid volunteering days and a charitable donation matching program. - Remote-first flexibility: Freedom to work remotely, with the option to use designated office locations for in-person collaboration. - Collaborative culture: Opportunities to connect with colleagues through virtual and in-person activities, team celebrations, and company events. - Inclusive workplace: Commitment to equal employment opportunities and reasonable accommodations throughout the recruitment process.