Quantitative Finance Engineer with Full-Stack Expertise

Ricky Ansari

Irvine, CA 949-***-**** *.******@********.*** linkedin.com/in/rickyansari rickyansari.com EDUCATION

University of Southern California Los Angeles, CA

M.S., Computational Finance; GPA: 3.93 / 4.00 Aug. 2024 – Dec. 2025 Columbia University, School of Engineering and Applied Science New York, NY B.S., Applied Mathematics, Minor in Computer Science; GPA: 3.80 / 4.00 Aug. 2018 – May 2022 Egleston Scholar (Top 1% of Columbia Engineering applicants); Columbia Data Science Society; Columbia Financial Engineering Group EXPERIENCE

Rainmaker Market Systems Remote

Software Engineer — Financial Technology Platform Sep. 2025 – Jan. 2026

• Built the central engine transforming unstructured financial data into profile vectors for 75 criteria, computing pairwise similarities, and serving ranked matches via paginated REST APIs with Redis caching — achieved sub-50ms P95 latency across 10K+ profiles.

• Designed full-stack infrastructure (React/Next.js, Node.js/Express, MongoDB) with reusable component library, aggregation pipelines, and compound indexing, enabling rapid research-to-production deployment with Zod schema validation for 300+ fields. Houlihan Lokey Los Angeles, CA

Quantitative Valuation Summer Analyst May 2025 – Jul. 2025

• Performed mark-to-market valuations for private equity and hedge fund portfolios across equity, credit, and derivative instruments using DCF, Monte Carlo simulation, Black-Scholes and binomial lattice option pricing, and credit yield-spread decomposition.

• Implemented a Snowflake Document AI + Snowpark Python pipeline ingesting raw valuation PDFs, performing OCR extraction, mapping fields to canonical schema, and writing completed models — reduced model-build time by 60%.

• Built a Python framework automating equity risk premium estimation and report generation, replacing 5-hour manual process. AIG (American International Group) New York, NY

Data Science Associate — Quantitative Analytics Jul. 2022 – Jul. 2024

• Engineered Python pipelines computing 25+ KPIs, fitting parametric distributions to premium-collection curves, estimating claim severity tail risk, and running Kaplan-Meier survival analysis on claim resolution times; automated threshold-breach alerting.

• Redesigned legacy dimensional data models, rewrote stored procedures, and added targeted indexes after profiling query execution plans — reduced runtime by 40% and eliminated all data integrity bugs across reporting feeds for C-suite leadership.

• Delivered dynamic Power BI and Tableau dashboards serving actuarial, underwriting, and executive stakeholders in 50+ countries. Compak Asset Management Newport Beach, CA

Quantitative Trading & Portfolio Management Intern Jun. 2020 – Aug. 2020; Jun. 2021 – Aug. 2021

• Built a regime-switching signal ensemble in Python to rebalance across equity, fixed income, and cash; tuned entry/exit thresholds via walk-forward optimization over 500+ parameter configurations; evaluated alpha candidates with rank IC and decay analysis.

• Designed a multi-factor scoring engine computing cross-sectional z-score rankings and sorting stocks into quintile buckets; backtested long/short quintile spread returns and benchmarked Sharpe, Sortino, and drawdown metrics against sector baselines. Mandli Group, Columbia Engineering Department of Applied Mathematics Remote Computational Geophysics Research Intern Jun. 2020 – Aug. 2020; Jun. 2021 – Aug. 2021

• Developed adaptive mesh refinement (AMR) configurations within the GeoClaw simulation package; designed hierarchies that dynamically refine grid cells based on error estimates, achieving 3–4x improvements in spatial accuracy and runtime.

• Backtested the LISFLOOD hydrological model against observed flood data from Hurricane Harvey; optimized simulation codebase via early-exit logic and vectorized NumPy implementations, reducing runtime by 10% and improving numerical accuracy by 15%. SELECTED PROJECTS

Multi-Asset Risk & Portfolio Analytics Platform Python, FastAPI, Next.js, CVXPY, PyTorch, LLMs, GenAI, Ray, Docker, Kubernetes

• Multi-agent GenAI risk engine with ReAct reasoning, Dual LLM prompt-injection defense, convex portfolio optimization, VaR/CVaR stress testing, factor models, Brinson-Fachler attribution, HMM regime detection, GNN contagion analysis, and SHAP explainability. Live Algorithmic Trading System Python, TensorFlow, AWS SageMaker, OpenVINO, Kafka, Redis, Alpaca API, Numba

• End-to-end trading system ingesting real-time market data for 50 stocks, engineering 12 features per asset, and generating signals with a custom temporal attention encoder; OpenVINO INT8 compilation for 4x latency reduction; Alpaca API deployment. TECHNICAL SKILLS

Machine Learning: PyTorch, TensorFlow, JAX, scikit-learn, XGBoost, LightGBM, CVXPY, Transformers, NumPy, Pandas, Polars, Numba Programming: Python, C, C++, CUDA, Rust, Java, Scala, Go, OCaml, Julia, Mojo, R, SQL, KDB+/Q, TypeScript, JavaScript; Node.js/Express, React/Next.js, FastAPI, Spring Boot; gRPC/Protobuf, FlatBuffers, WebSocket, FIX, ZeroMQ, Arrow Flight, GraphQL; pybind11, DPDK, MPI Tools & Infastructure: AWS, GCP, Terraform, Docker, Kubernetes, Helm, Nginx, Linux, Git, CI/CD, Grafana; PostgreSQL, MongoDB, MySQL, Redis, DuckDB; Kafka, Spark, Airflow, dbt, Ray, Apache Arrow, Snowflake; Bloomberg Terminal, Excel, VBA, Power BI, Tableau

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