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Data Scientist at Marsh McLennan. Location Information: Mexico City - Paseo. . Company:. MMC Corporate. Description:. Data Scientist. We are seeking a Data Scientist to join our team at GC. This role will be based in Mexico City. This is a hybrid role that has a requirement of working at least three days a week in the office. As a Data Scientist at GC, you you'll leverage cutting-edge machine learning to enhance data-driven products for a global leader in risk management and reinsurance. This cross-functional environment offers the opportunity to contribute throughout the full AI solution lifecycle while solving complex problems in the reinsurance domain.. We will count on you to:. Back Incorporate cutting-edge business intelligence, machine learning, and alternative data practices into our wider organization to drive efficiencies and generate revenue. Own the complete model lifecycle from data wrangling and development through training and monitoring. Stay current with the latest ML/AI innovations and strategically apply these advancements to enhance GC's business and data strategy. Build and improve our internal ML/AI private packages and tooling. Establish and maintain strong relationships with internal team members and external clients. Evangelize ML/AI techniques and best practices throughout our analytics and engineering teams. What you need to have:. 4-5 years of experience developing and deploying ML models in production environments. Significant expertise with Python data science ecosystem including pandas, scikit-learn, numpy, and deep learning frameworks (TensorFlow, . PyTorch. or Hugging Face). Strong SQL skills for data manipulation and analysis. Ability to produce production-quality code with adherence to engineering best practices (unit testing, code reviews, CI/CD . pipelines. ). Exceptional analytical abilities and intellectual curiosity demonstrated through academic or professional work. Hands-on experience developing AI/ML technologies such as LLM inference, similarity search, vector databases, guardrails, and memory systems using Python. Advanced Level of English is a Must. What makes you stand out:. Experience with Databricks and its ML ecosystem. Hands-on experience developing AI/ML technologies. Experience fine-tuning open source LLMs for specific business applications. Why join our team:. We help you be your best through professional development opportunities, interesting work and supportive leaders.. We foster a vibrant and inclusive culture where you can work with talented colleagues to create new solutions and have impact for colleagues, clients and communities.. Our scale enables us to provide a range of career opportunities, as well as benefits and rewards to enhance your well-being.. Guy Carpenter, a business of Marsh McLennan (NYSE: MMC), is a leading global risk advisory and reinsurance specialist and broker. Marsh McLennan is a global leader in risk, strategy and people, advising clients in 130 countries across four businesses: Marsh, Guy Carpenter, Mercer and Oliver Wyman. With annual revenue of $24 billion and more than 90,000 colleagues, Marsh McLennan helps build the confidence to thrive through the power of perspective. For more information, visit guycarp.com, or follow on LinkedIn and X.. Marsh McLennan is committed to creating a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age, background, disability, ethnic origin, family duties, gender orientation or expression, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, or any other characteristic protected by applicable law.. Marsh McLennan is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh McLennan colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office-based teams will identify at least one “anchor day” per week on which their full team will be together in person.. .