Technical Program Lead

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you'd like, where you'll be supported and inspired by a collaborative community of colleagues around the world, and where you'll be able to reimagine what's possible. Join us and help the world's leading organizations unlock the value of technology and build a more sustainable, more 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 the development of advanced analytics, machine learning, experimentation, and AI solutions that improve customer experience, marketing performance, operational efficiency, and business growth. This role requires deep expertise in Data Science, technical leadership, model governance, and production Machine Learning environments.

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

- 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

- 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

- 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

- 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 – 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 alike.

- Mentor and coach – Invested in developing talent and fostering technical excellence.

- Innovative problem solver – 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

- AI Solutions

- Minimum 3 years of experience providing technical leadership for production Machine Learning teams.

Required Skills

Applied Data Science & Statistics

- Expertise in:

- Predictive Analytics

- Prescriptive Analytics

- Segmentation

- Recommendation Systems

- Optimization

- Causal Inference

- Experimental Design

- Deep understanding of statistical modeling and business-driven analytics.

Technical Leadership

- Proven ability to influence technical direction and business decisions.

- Experience mentoring Data Scientists and leading technical reviews.

- Strong stakeholder management and executive communication capabilities.

Programming & Analytics Engineering

- Advanced proficiency in:

- Python

- SQL

- Ability to review and contribute to production-grade analytical code.

- Experience building scalable analytical solutions.

Cloud Analytics & Data Platforms

- Strong experience with:

- Snowflake

- Cloud-Based Machine Learning Platforms

- Enterprise Data Platforms

- Experience with Databricks, Apache Spark, or equivalent technologies is highly desirable.

Experimentation & Causal Measurement

- Expertise in:

- A/B Testing

- Incrementality Analysis

- Holdouts

- Lift Measurement

- Causal Modeling

- Ability to design measurement frameworks that demonstrate business value.

MLOps & Lifecycle Management

- Understanding of:

- GitHub

- CI/CD

- Model Deployment

- Monitoring

- Retraining

- Production ML Environments

- Familiarity with model lifecycle governance and operational best practices.

Responsible AI

- Experience defining AI governance frameworks and validation standards.

- Ability to create guardrails that support ethical, transparent, and accountable AI implementation.

Preferred Skills

- Marketing Analytics.

- Customer Analytics.

- Sales Analytics.

- Customer Lifetime Value (CLV) Modeling.

- Recommendation Systems and Personalization Engines.

- Marketing Mix Modeling.

- Propensity Modeling.

- Snowflake Cortex.

- Snowpark ML.

- Model Risk Management Frameworks.

- 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 dual transition to a digital and sustainable world while creating tangible impact for enterprises and society. It is a responsible and diverse organization with more than 340,000 team members in over 50 countries.

With more than 55 years of heritage, Capgemini helps clients unlock the value of technology through end-to-end services and solutions, leveraging capabilities in strategy and design, engineering, AI, generative AI, cloud, and data, combined with deep industry expertise and a strong partner ecosystem.

At Capgemini Mexico, we are committed to fostering a diverse and inclusive workplace where everyone has equal opportunities. We welcome applications from all qualified candidates and evaluate them based on merit, skills, qualifications, and experience relevant to the role.