• Apply statistical and econometric modeling, time series analysis, optimization methods, and generative AI to develop improved approaches to demand modeling, elasticity estimation, price recommendation, competition analytics, and related challenges. • Develop reusable tools, frameworks, and components to support and accelerate delivery of value from data science. • Deploy scalable and maintainable solutions to cloud environments, leveraging version control practices and robust code quality and documentation standards. • Work independently and as part of the Data Science Research team, contributing to research prioritisation and technical direction in collaboration with the Director and Product leadership. • Demonstrate technical excellence by delivering high-quality, innovative solutions and championing best practices to ensure research outputs are reproducible, scalable, and maintainable. • Contribute to evaluation and adoption of appropriate tools, programming languages, and technologies. • Mentor junior team members, sharing knowledge and supporting their professional development. • Identify and contribute to patent opportunities and the company's intellectual property portfolio. • Collaborate across Development, Product, Analytics, and Commercial teams to ensure solutions address business challenges and stakeholder objectives. • Partner with operational teams to ensure data pipelines and infrastructure are robust, secure, and optimized for research and production workloads. • Communicate technical concepts and outcomes effectively to both technical and non-technical audiences. • Stay current with developments in data science, machine learning, and AI, applying state-of-the-art techniques to project work. • Develop comprehensive knowledge of the retail fuel industry to understand domain challenges and help guide product innovation. • Share knowledge and promote best practices across the team and company.

• Design, build, and maintain pricing and risk models for a wide range of P&C insurance products • Apply machine learning and AI algorithms to enhance pricing accuracy and risk segmentation • Lead forecasting initiatives and align insights with broader strategic and financial planning • Evaluate Wakam’s aggregate risk exposure to identify potential risk accumulation and ensure alignment with internal limits and reinsurance treaties • Track and analyze exposure levels across portfolios, focusing on sum insured, policy limits, and risk accumulation across geographical areas, product lines, and coverages • Automate and optimise the model development lifecycle • Ensure regulatory compliance and contribute to model governance best practices • Mentor junior team members and foster a high-performance analytics culture

RH: Lessly Mujica Location: any state of Mexico General Description: As a Lead Data Scientist, you will be at the forefront of our data science initiatives, leveraging advanced machine learning techniques and cloud technologies to deliver impactful solutions for our clients. You will lead the development and deployment of production-grade machine learning models using Azure's ecosystem, driving innovation and excellence in our data science practice. In this role you will play a key role in: - Leading the end-to-end machine learning lifecycle from problem formulation to production deployment, ensuring high-quality, scalable, and maintainable solutions - Architecting and implementing advanced ML pipelines using Azure ML, Databricks, and related services while adhering to best practices in MLOps - Developing and optimizing tabular machine learning models (XGBoost, LightGBM, Random Forest, Logistic Regression) with focus on explainability, performance tuning, fairness, and robustness - Building and maintaining scalable data processing pipelines using PySpark and Spark SQL to support model training and inference - Deploying models as REST APIs through Azure API Management and establishing monitoring frameworks for production models - Creating comprehensive documentation including model cards, validation reports, runbooks, lineage documentation, and operational guidelines - Leading cross-functional collaboration with platform teams, data engineers, governance specialists, and domain experts - Mentoring junior data scientists and establishing best practices for the team - Communicating complex technical concepts to stakeholders and influencing architectural decisions Your profile - Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or related technical field - 15+ years of overall IT experience with 6+ years delivering production-grade ML models using enterprise cloud platforms, including at least 3+ years in Azure ecosystems - Demonstrated expertise in Azure ML (workspaces, pipelines, model registry, endpoints), Azure Databricks (PySpark, Spark SQL, MLflow), Azure Data Factory (data pipelines & orchestration), Azure Data Lake (ADLS Gen2), and Azure DevOps CI/CD - Deep knowledge of tabular machine learning techniques including XGBoost, LightGBM, Random Forest, and Logistic Regression, with focus on explainability, performance tuning, fairness checks, and robustness validation - Strong Python development skills with experience building scalable data and ML pipelines using PySpark/Spark SQL - Experience working with Medallion Architecture (Bronze/Silver/Gold) and semantic layers - Proven ability to deploy models as REST APIs through API gateways or Azure API Management - Experience producing comprehensive documentation including model cards, validation reports, runbooks, lineage documentation, and operational guidelines - Familiarity with responsible AI practices, fairness testing, and SHAP explainability - Experience working in agile environments with SIT, UAT, and phased releases - Strong stakeholder management skills and ability to lead cross-functional collaboration - Experience in regulated domains (pharma, healthcare, clinical operations, CRO) is highly desirable - Knowledge of validation processes like GxP and CSV is a plus What you'll love about working here At Capgemini LATAM, we aim to attract the best talent and are committed to creating a diverse and inclusive work environment, so there is no discrimination based on race, sex, sexual orientation, gender identity or expression, or any other characteristic of a person. All applications welcome and will be considered based on merit against the job and/or experience for the position About Capgemini Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fuelled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.

Senior Data Scientists lead data analysis projects and provide strategic insights. Responsibilities include designing complex models, mentoring junior data scientists, and ensuring the quality of analytical work. You will work closely with business leaders to understand needs and deliver actionable insights. Extensive experience in data science and leadership skills are required.