Sathiya Sundarajan is a Director leading an AI-native Systematic QIS Desk in San Francisco, bringing 14 years of experience at the intersection of systematic macro, stochastic volatility, and computational finance. He builds research-to-execution stacks from the ground up—combining regime-aware signal generation, SVI/SSVI volatility modeling, and convex derivatives construction to isolate durable alpha across market regimes. Prior roles span technology leadership in healthcare and large-scale search and analytics platforms at Morgan Stanley, Lucidworks, Cisco, and others, reflecting deep expertise in production-grade data systems. He also co-founded a stealth NLP startup, giving him practical product and applied ML experience beyond pure quant research. Known for translating advanced stochastic frameworks into traded strategies, he blends quantitative rigor with software engineering discipline. Based in San Francisco, he pairs a Master’s from the University of Melbourne and an engineering background with a proven track record of shipping complex, high-throughput systems.
14 years of coding experience
16 years of employment as a software developer
B.E, Bachelor of Engineering, B.E, Bachelor of Engineering at University of Madras
A Rank Component, that extends Solr Search component and uses the results returned by to compute the rank. Rank component internally depends on Solr's search & facet components. Second, Simple Rank Engine that implements various ranking strategies. Supported ranking strategies are Dense, Standard Competition, Modified Competition, Fractional & Ordinal. Good explanation of ranking strategies can be found at Wikipedia
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