Lead Data Scientist in Haciendas del Canada, Nuevo León at Jobgether

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.

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.

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.