Job description
At Axiom Analytics, we solve complex problems at the intersection of mathematics, data science, and engineering. We are seeking a talented Mathematics Research Scientist to join our Cambridge, MA team and contribute to modeling initiatives with tangible impact.
This role blends advanced theory with practical application: you will develop rigorous mathematical models, analyze high-volume data, and collaborate with software engineers and domain experts to deliver robust, scalable solutions.
As part of the team, you will have opportunities to publish findings, present at conferences, and mentor junior scientists. The ideal candidate combines strong theoretical foundations with hands-on programming and a collaborative mindset.
Responsibility
- Design and implement mathematical models to solve real-world problems across finance, data science, or engineering domains.
- Develop and analyze numerical algorithms, ensuring accuracy, stability, and efficiency for large-scale datasets.
- Collaborate with data scientists, software engineers, and domain experts to translate theory into production-ready solutions.
- Validate models using rigorous statistical methods and cross-validation techniques.
- Communicate complex mathematical concepts to non-specialist stakeholders through clear visualizations and reports.
- Mentor junior researchers and contribute to internal knowledge sharing through documentation and code reviews.
- Publish research findings in peer-reviewed venues and present at internal and external forums.
- Contribute to project scoping, proposal writing, and evaluation of model performance against business metrics.
Qualification
- PhD in Mathematics, Applied Mathematics, Statistics, or a closely related field; or equivalent research experience.
- Strong theoretical background in numerical analysis, optimization, probability, and statistical inference.
- Proven ability to design and analyze mathematical models with experimental validation.
- Proficiency in Python (NumPy, SciPy), R, MATLAB, or Julia; experience with scientific computing.
- Experience with data-driven modeling, machine learning, or probabilistic programming is a plus.
- Strong communication skills; ability to present complex concepts to non-technical audiences.
- Collaborative mindset with demonstrated ability to work across disciplines.
- Experience publishing research or presenting at conferences is preferred.