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Lead Data Scientist
| Company | Marsh & McLennan |
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| Location | New York, NY, USA |
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| Salary | $143300 – $286600 |
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| Type | Full-Time |
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| Degrees | Master’s, PhD |
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| Experience Level | Senior, Expert or higher |
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Requirements
- Minimum Master’s Degree in Computer Science, Data Science, or a related field.
- Minimum of 7 years of experience in AI/ML and data science, with a proven track record of developing and implementing machine learning models in a corporate environment.
- High Proficiency in Python, strong understanding of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, MLFlow); experience in AI Framework (Langchain, Tensors and Vectors), experience with database and data structures (Vector DB, Mongo, Postgres, Graph, NetworkX), data visualization tools (e.g., Tableau, Power BI) and big data technologies (e.g., Databricks, PySpark).
- Experience developing in public cloud (AWS, Azure) and knowledge of Docker Containers.
- Demonstrated experience in leading at least a 3-5 person team, with experience in mentoring and developing talent.
Responsibilities
- Collaborate closely with FP&A stakeholders to understand their strategic vision and analytics needs, translating these concepts into actionable projects.
- Develop and execute a roadmap for analytics initiatives that align with FP&A’s long-term goals while ensuring timely delivery of short-term projects.
- Lead the design and implementation of advanced machine learning models and algorithms that provide insights into financial performance, forecasting, and decision-making.
- Manage and mentor a team of data scientists and analysts, fostering a culture of innovation, collaboration, and continuous learning.
- Work closely with finance, IT, and other departments to ensure that analytics solutions are developed with a comprehensive understanding of business needs and technical requirements.
Preferred Qualifications
- PhD in Computer Science, Data Science, or a related field from a top-tier U.S. university is highly preferred.
- Published Whitepapers and Scientific Journals with citation.
- Experience managing global resources and working in cross-functional teams across different geographies.
- A strong interest in staying abreast of industry trends and emerging technologies in AI/ML and data science, applying this knowledge to enhance analytics capabilities.
- Ability to approach complex problems with a research-oriented mindset, leveraging advanced analytical techniques to drive business insights.
