Choosing Capgemini means choosing a company where you will be empowered to shape your career, supported by a collaborative global community, and able to help leading organizations unlock the value of technology while building a more sustainable and inclusive world.
Your Role
As a MarTech Data Science Lead at Capgemini, you will serve as the senior technical authority responsible for shaping the vision, roadmap, methodologies, and delivery standards of the MarTech Data Science portfolio. You will drive advanced analytics, machine learning, experimentation, and AI solutions that improve customer experience, marketing performance, operational efficiency, and business growth.
You will partner with business and technology leaders to identify high-impact opportunities, guide technical decisions, establish best practices, and ensure analytical solutions generate measurable business value.
Responsibilities & Scope
Data Science Strategy & Vision
- Define and communicate the MarTech Data Science technical roadmap.
- Identify and prioritize opportunities where advanced analytics and machine learning can drive measurable business outcomes.
- Establish long-term strategies for analytics, experimentation, machine learning, and AI initiatives.
- Align Data Science investments with business objectives and enterprise priorities.
Advanced Analytics & Machine Learning Leadership
- Lead problem definition, solution design, methodology selection, and model architecture decisions.
- Drive the development of predictive models, prescriptive analytics, recommendation engines, customer segmentation models, optimization models, propensity models, and experimentation frameworks.
- Define success metrics and measurement strategies for analytical initiatives.
- Guide teams through model validation, deployment, monitoring, and continuous improvement.
Experimentation & Measurement
- Establish frameworks for A/B testing, incrementality measurement, holdout design, uplift analysis, causal inference, and experimental design.
- Ensure analytical rigor in evaluating marketing, customer, and operational initiatives.
- Define standards for measuring business impact and ROI.
Model Governance & Responsible AI
- Establish standards for model quality, reproducibility, documentation, monitoring, validation, governance, and lifecycle management.
- Lead the implementation of Responsible AI practices across Data Science initiatives.
- Define governance controls that promote transparency, accountability, explainability, and compliance.
- Strengthen model QA processes and model-risk management practices.
Technical Leadership & Mentorship
- Serve as the senior technical advisor for the Data Science team.
- Guide architectural decisions and resolve complex technical challenges.
- Mentor Data Scientists and Analytics professionals.
- Promote peer reviews, knowledge sharing, and continuous improvement.
- Foster a culture of innovation, scientific rigor, and business impact.
Stakeholder Engagement & Executive Advisory
- Partner with Product, Marketing, Sales, Analytics, Data Engineering, MLOps, and Technology leaders.
- Translate sophisticated analytical approaches and technical risks into clear executive recommendations.
- Influence strategic decision-making through data-driven insights.
- Present results and recommendations to senior leadership and executive audiences.
MLOps & Production AI
- Review and guide MLOps architectures and production deployment strategies.
- Support best practices for CI/CD, model deployment, monitoring, retraining, environment management, and production support.
- Ensure AI and ML solutions are scalable, maintainable, and production-ready.
Your Profile
- Thought leader in Data Science, passionate about leveraging analytics and AI to solve complex business problems.
- Strategic advisor who connects advanced analytics initiatives to measurable business outcomes.
- Technical expert comfortable leading enterprise-scale machine learning and AI programs.
- Influential communicator able to engage technical teams and executive stakeholders.
- Mentor and coach invested in developing talent and fostering technical excellence.
- Innovative problem solver who combines scientific rigor with practical business thinking.
Qualifications & Preferred Skills
Required Qualifications
- Bachelor's or Master's degree in Data Science, Statistics, Applied Mathematics, Computer Science, Engineering, Economics, Operations Research, or a related quantitative discipline.
- 8–12+ years of experience in Data Science, Machine Learning, Advanced Analytics, or AI Solutions.
- At least 3 years of experience providing technical leadership for production Machine Learning teams.
Required Skills
- Applied Data Science and statistics expertise, including predictive analytics, prescriptive analytics, segmentation, recommendation systems, optimization, causal inference, and experimental design.
- Strong understanding of statistical modeling and business-driven analytics.
- Proven ability to influence technical direction and business decisions.
- Experience mentoring Data Scientists and leading technical reviews.
- Strong stakeholder management and executive communication capabilities.
- Advanced proficiency in Python and SQL.
- Ability to review and contribute to production-grade analytical code and build scalable analytical solutions.
- Strong experience with Snowflake, cloud-based machine learning platforms, and enterprise data platforms.
- Experience with Databricks, Apache Spark, or equivalent technologies is highly desirable.
- Expertise in A/B testing, incrementality analysis, holdouts, lift measurement, and causal modeling.
- Understanding of GitHub, CI/CD, model deployment, monitoring, retraining, production ML environments, model lifecycle governance, and operational best practices.
- Experience defining AI governance frameworks and validation standards.
Preferred Skills
- Marketing analytics, customer analytics, and sales analytics.
- Customer Lifetime Value modeling.
- Recommendation systems and personalization engines.
- Marketing mix modeling and propensity modeling.
- Snowflake Cortex and Snowpark ML.
- Model risk management frameworks and enterprise AI governance programs.
What You'll Love
- Opportunity to lead enterprise-scale Data Science and AI initiatives.
- Exposure to cutting-edge Machine Learning, Advanced Analytics, and Responsible AI programs.
- Collaboration with global Product, Data, Analytics, and Technology leaders.
- Access to industry-leading certifications, technical academies, and leadership development programs.
- Career growth opportunities within Capgemini's global Data & AI practice.
- Inclusive and innovative environment focused on continuous learning and measurable business impact.
Need to Know
Career Development in Spanish & English: Access to technical certifications, mentorship programs, leadership development tracks, and continuous learning opportunities focused on AI, Machine Learning, Analytics, and Data Science.
Community Engagement: Participate in innovation challenges, AI communities, technical forums, knowledge-sharing sessions, volunteering initiatives, and global Capgemini events.
About Capgemini
Capgemini is a global business and technology transformation partner helping organizations accelerate their digital and sustainable transitions. It has more than 340,000 team members in over 50 countries and more than 55 years of heritage, with capabilities spanning strategy and design, engineering, AI, generative AI, cloud, and data.
At Capgemini Mexico, the company is committed to fostering a diverse and inclusive workplace where everyone has equal opportunities.