Senior Data Scientist

Location: Montreal

Compensation: $, - , plus up to $20, bonus, capped at %

Job Summary

Giesecke+Devrient is a globally leading Security Tech company. We are seeking a highly analytical and execution-focused Senior Data Scientist to join G+D’s new AI Hub. The ideal candidate will combine strong expertise in machine learning, statistical modeling, experimentation, and business problem solving with the ability to translate complex enterprise challenges into scalable AI solutions that deliver measurable impact. The role is designed to deliver immediate value through operational efficiencies and intelligent decision support, while simultaneously contributing to the development of differentiated AI capabilities and products within the Security Tech domain.

This role contributes directly to G+D’s enterprise-wide AI transformation and strategic value targets. As part of G+D’s global AI transformation, the Senior Data Scientist will collaborate closely with AI Engineers, ML Engineers, Data Engineers, product and project managers, and executive stakeholders to unlock enterprise-wide value through data science and machine learning. This role will function within a global matrix organization and report into the AI Hub leadership.

Primary Responsibilities

Own data science initiatives end-to-end from problem framing through production deployment and performance monitoring

Data Science & Machine Learning Development

Design, develop, and validate machine learning models, predictive models, and advanced analytical solutions

Perform exploratory data analysis, feature engineering, and model performance evaluation

Develop statistical, forecasting, classification, anomaly detection, optimization, and causal inference models

Establish experimentation and A/B testing frameworks to measure business impact

AI Product Development & Delivery

Support the development and deployment of AI solutions across internal workflows and customer-facing products

Work closely with engineering teams to productionize models and analytical pipelines

Ensure solutions are scalable, reusable, and aligned with enterprise standards

Contribute to the integration of models into enterprise data and AI platforms

Governance, Ethics & Compliance

Ensure responsible AI practices including explainability, bias checks, traceability, and validation

Support model monitoring, drift detection, and lifecycle governance

Ensure compliance with global data privacy, security, and regulatory standards

Contribute to documentation and auditability standards for production AI systems

Stakeholder Engagement

Partner with business unit leaders and functional teams to identify and prioritize high-value use cases

Translate business challenges into measurable analytical solutions

Present insights, model outputs, and recommendations to technical and non-technical stakeholders

Influence the direction of AI and data science initiatives through technical leadership and business partnership

Team Building & Leadership

Contribute to building a high-performing AI and Data Science capability within the AI Hub

Mentor junior team members and promote best practices

Foster a culture of experimentation, rigor, and collaboration

Promote knowledge sharing across global and local teams

Qualifications, Experience and Educational Requirements

Work Experience:

Five + years of experience in Data Science, Machine Learning, or Advanced Analytics, with proven experience delivering enterprise-scale AI or ML initiatives

Skills and

Competencies:

Strong expertise in machine learning, statistical inference, predictive modeling, experimentation / A/B testing, causal inference, forecasting, optimization, and anomaly detection

Hands-on experience with Python, pandas, Num Py, scikit-learn, and SQL

Experience with one or more of:
PyTorch, Tensor Flow, XGBoost, LightGBM, time series forecasting frameworks

Strong understanding of cloud-native data and AI platforms

Experience working in regulated industries is a plus

French speaking is an asset

Education:

PhD or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related quantitative field preferred