Quant Scientist (Remote, US Eastern Time Zone)
Key Skills for This Role
Full Job Posting
Key Responsibilities
- Critically evaluate ML model outputs to ensure alignment with financial theory and real-world market dynamics, while accounting for client-specific objectives, constraints, and investment frameworks.
- Translate research signals into actionable investment strategies and portfolio construction frameworks.
- Collaborate with ML researchers to refine models, incorporating financial domain expertise and contributing to model design where needed.
- Design, prototype, and scale quantitative models using Python and Java, maintaining a high standard for code quality and modularity.
- Contribute to the financial validation layer of the R&D cycle by developing and maintaining test frameworks that identify inconsistencies and support continuous model improvement.
- Translate complex portfolio objectives into rigorous, testable modeling specifications that bridge the gap between investment intent and algorithmic execution.
- Collaborate across technical workstreams to ensure research outputs are aligned with investment objectives and successfully integrated into production workflows.
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Requirements
- Degree in Finance, Quantitative Finance, Financial Engineering, Mathematics, or a related field.
- Understanding of portfolio construction, asset allocation, and risk management
- Solid Python/Java programming skills , with experience in financial modeling, data analysis, and working with ML-driven workflows.
- Experience interpreting, validating, or stress-testing quantitative or machine learning models (e.g., backtesting, scenario analysis, or model diagnostics).
- Ability to bridge finance and technology: translate investment concepts into technical requirements and challenge model outputs using real-world financial intuition.
- Strong analytical mindset with the ability to communicate complex quantitative insights clearly to both technical and business stakeholders.
- Fluent in English (written and spoken).
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Bonus Points
Solid understanding of Git-based workflows (branching, code reviews, version control) in collaborative research or production environments.
Experience leveraging LLMs and AI coding tools (e.g., Claude Code, GitHub Copilot) to accelerate prototyping, refactor code, and optimize algorithmic performance.
Proven ability to thrive in high-pressure, collaborative environments , delivering precise results under tight market-driven or project deadlines
About MDOTM
AI-driven investment solutions for banks, insurers, asset managers, wealth managers, and institutional investors.
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