Company:
Guy Carpenter
Description:
What can you expect?
- Be part of a small, high-impact team working at the intersection of data engineering and data science.
- Design and deliver scalable data pipelines, production ML models, and data products that drive measurable business outcomes.
- Work with semi- and unstructured data, including LLMs and NLP techniques.
- Collaborate closely with product managers, senior engineers, and business stakeholders.
What is in it for you?
- Opportunity for technical and career growth by working end-to-end on ML and data projects.
- Hands-on experience with ELT/ETL pipelines, modeling, deployment, and production monitoring (MLOps).
- Exposure to cloud platforms and modern ML frameworks.
- Chance to deliver data products that influence business decisions and results.
We will count on you to:
- Implement and maintain scalable ELT/ETL data pipelines.
- Contribute to the development, validation, and deployment of ML models with a focus on reproducible training and CI/CD.
- Apply MLOps best practices for model packaging, deployment, and monitoring.
- Build and maintain data models and feature stores to ensure data integrity and quality.
- Deliver data products (APIs, dashboards, notebooks) that translate models and analytics into actionable outcomes.
- Evaluate performance, scalability, and cost-efficiency of data and ML systems in cloud environments.
- Define and maintain operational standards, including logging, alerting, and documentation.
- Collaborate with stakeholders to define requirements and success metrics.
What you need to have:
- Professional experience in data engineering, data science, or ML engineering (including internships).
- Degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field — or equivalent experience.
- Proficiency in Python and SQL.
- Familiarity with unit, integration, and data quality testing.
- Knowledge of data transformation frameworks (e.g., dbt).
- Experience with core ML frameworks (e.g., scikit-learn, PyTorch, transformers).
- Exposure to or strong interest in MLOps concepts (CI/CD for ML, model deployment, production monitoring).
- Interest in NLP and text-based ML tools (regular expressions, NLTK, spaCy).
What makes you stand out:
- Experience with cloud data platforms (Databricks, Snowflake, BigQuery).
- Demonstrated experience deploying ML models to production.
- Prior experience on a data team in technology, consulting, or startup environments.
- Domain knowledge or strong interest in insurance/reinsurance analytics and risk products.
- Familiarity with BI tools (Looker, Tableau, Power BI).
- Experience with data-labeling frameworks (e.g., LabelStudio).
- Understanding of ETL/ELT patterns and REST APIs.
At Guy Carpenter, a Marsh business, you can be your best. We work on challenges that matter with colleagues who help bring out our best. Our uniquely collaborative environment will empower you to focus on your personal and professional success, learning from top specialists in the (re)insurance industry and leading you towards a rewarding and impactful career.
Guy Carpenter is a business of Marsh (NYSE: MRSH), a global leader in risk, reinsurance and capital, people and investments, and management consulting, advising clients in 130 countries. With annual revenue of over $27 billion and more than 95,000 colleagues, Marsh helps build the confidence to thrive through the power of perspective. For more information about Guy Carpenter, visit guycarp.com, or follow us on LinkedIn and X.
Marsh 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 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 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.