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Arta Finance is building a digital family office platform that democratizes sophisticated investment strategies and alternative assets previously available only to ultra-high-net-worth individuals. The company uses AI and digital tools to help clients grow and protect wealth through intelligent portfolio construction and automated trading.
You will join a small, high-impact team responsible for the quantitative systems that power real client portfolios at scale. Reporting to the CIO, you'll design and build the models that determine target portfolio allocations and the production systems that execute trades efficiently. This role bridges quantitative research and software engineering—you'll move seamlessly between portfolio theory, optimization, and reliable production systems.
Key responsibilities include:
- Designing and improving models for target portfolio allocations, balancing risk, return, and client-specific constraints
- Building systems that translate allocations into real trades with emphasis on tax efficiency (tax-loss harvesting) and cost-aware execution
- Applying portfolio management techniques including optimization, factor-based risk modeling, and statistical estimation
- Backtesting and validating new models against historical data before deployment to live portfolios
- Partnering with investment, product, and engineering teams to ship production-quality systems
- Leveraging AI coding tools as part of daily workflow for faster research and development
You bring 5+ years of experience working close to markets or portfolios—as a quant researcher, trader, or in an advisory/PM-facing role. You have strong quantitative finance fundamentals (portfolio theory, optimization, risk modeling), rigorous math skills (linear algebra, probability, statistics), and proven software engineering ability to take models from research to production. You understand tax-aware investing concepts, are fluent with AI coding tools, and communicate effectively with both investment leadership and engineering teams. You thrive in fast-paced startup environments with high ownership and comfort working independently.
