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Capgemini

Global consulting, technology services, outsourcing

Senior Data Scientist

Applied Machine LearningUpdated on Nov 14
Full-timeVila Nova de Gaia, Portugal

About the Job

Requirements

  • Strong knowledge of statistics, probability, experimental design, power analysis, model validation, and machine learning.
  • Experience working in cross-functional environments with strong stakeholder engagement.
  • Strong expertise in advanced machine learning and statistical modelling techniques, including gradient boosting, classical machine learning, and time-series forecasting.
  • Proficiency in Python and SQL for data analysis, modelling, and production-grade solutions.
  • Hands-on experience with Spark and other distributed data processing frameworks in large-scale environments.
  • Solid understanding of Git-based development practices, version control, and reproducible machine learning workflows.
  • Proven track record of deploying, monitoring, and maintaining machine learning models in production alongside engineering teams.
  • Strong knowledge of model evaluation, performance monitoring, drift detection, error analysis, and retraining strategies.
  • Experience conducting experimental design, A/B testing, control-group analysis, power analysis, and quasi-experimental methods.

Responsibilities

  • Lead the end-to-end data science lifecycle, from problem definition and data preparation to model deployment, performance monitoring, and continuous improvement.
  • Partner with Product Managers and business stakeholders to define success metrics, evaluate data sources, and translate business challenges into measurable analytical solutions.
  • Develop, validate, and deploy statistical and machine learning models for fraud, credit risk, pricing, churn, and mobility services, applying robust validation, experimentation, and A/B testing methodologies.
  • Collaborate with Analytics Engineers, Data Engineers, and ML Engineers to define features, data-quality requirements, and scalable AI and analytics solutions.
  • Deliver production-ready models, including monitoring, retraining criteria, drift analysis, and evaluation frameworks for large language model-based solutions.
  • Apply best practices in model validation, explainability, bias assessment, responsible AI, governance, and regulatory compliance.
  • Communicate methods, assumptions, uncertainty, trade-offs, and recommendations to technical and non-technical stakeholders.
  • Contribute to data science standards, reusable frameworks, code reviews, and mentoring initiatives.

Desired Qualifications

  • Experience with causal inference, fintech, payments, fraud, credit risk, pricing, mobility services, model risk management, or AI regulatory frameworks.
  • Familiarity with Databricks, Snowflake, and Azure Machine Learning.
  • Exposure to natural language processing, deep learning, and evaluation frameworks for large language model-based solutions.
  • Knowledge of causal inference techniques and advanced experimentation methodologies.
  • Active involvement in open-source initiatives, technical communities, or data science knowledge-sharing activities.

About the Company

Capgemini

Global consulting, technology services, outsourcing

Capgemini provides consulting, technology services, and outsourcing to help organizations transform digitally. Its offerings include strategy and transformation consulting, system integration, application development, IT infrastructure management, and managed IT operations. It differentiates itself with a global presence in over 50 countries and a focus on applying AI, cloud, and cybersecurity to real business problems. Its goal is to help clients navigate digital transformation and improve efficiency and innovation through tailored, contract-based or managed services.

Company size
10,001+
Company stage
IPO
Headquarters
Paris, France
Founded
1967

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