Daniel Stahl, Developer in Birmingham, AL, United States
Daniel is available for hire
Hire Daniel

Daniel Stahl

Verified Expert  in Engineering

Mathematical Finance Developer

Location
Birmingham, AL, United States
Toptal Member Since
August 25, 2021

Daniel is a full-stack developer with nine years of real-world experience in financial modeling and analytics and three years of experience developing big data applications in Spark and Hadoop. He currently leads a machine learning operations team that designs continuous delivery services for machine learning applications. While professionally focused on data and analytics, Daniel has a passion for building delightful interfaces to enable the broad consumption of complex mathematical models.

Data EngineeringGitSparkGitOpsLinuxSQLPythonReactNode.jsREST APIsAmazon Web Services (AWS)RustVisual Studio Code (VS Code)Google Cloud Platform (GCP)Scala

Availability

Part-time

Preferred Environment

Linux, Rust, Flutter, Spark, Python, Scala, Git, Kubernetes, Visual Studio Code (VS Code)

The most amazing...

...mobile app I've developed is a financial option pricing engine which efficiently computes calls and puts when the underlying asset follows a Levy process.

Work Experience

Head of Data Platforms

2016 - PRESENT
Regions
  • Built the data engineering and machine learning operations team from scratch.
  • Developed self-service continuous deployment pipelines to allow data scientists to safely deploy their models. Full provenance from data to code and model objects.
  • Successfully operationalized 100% of the models developed by the data science team over the last three years.
  • Developed data pipelines to transform raw source-system data into analytics-ready data marts.
Technologies: Spark, Spark ML, GitOps, Data Engineering, Kubernetes, Machine Learning Operations (MLOps), Apache Airflow

Senior Auditor

2014 - 2016
BB&T
  • Validated financial models, including economic capital, stress testing models, and market and option pricing models.
  • Performed data testing and provided self-service data extraction tools to auditors as part of the initial audit analytics team.
  • Developed full-stack applications for executing continuous testing on key data sources.
Technologies: React, Git, Node.js, SQL

Option Pricing Mobile Application

A Flutter app with a financial pricing engine back end written in Rust. It leverages the technique in Fang-Oosterlee's 2008 paper for efficiently inverting the characteristic function of a Levy process.

Efficient Computation of "Loss Distribution Approach" to Operational Risk

Using the results from my peer-reviewed paper on operational risk, I efficiently computed an extended "loss distribution approach" for computing the tail risk from operational risk.

Efficient Computation of Portfolio Credit Risk

Using the results from my peer-reviewed paper, this project computes the loan-level risk contributions efficiently for even large portfolios. The model includes the ability to incorporate liquidity risk in the calculation of credit economic capital.

Fixed-income VaR Calculation

Compute the value at risk (VaR) using Monte Carlo techniques for common fixed-income options, including caps and floors, swaptions, Eurodollar futures, and bond puts and calls.

Onkyo Remote Control

Created a plugin for the "Yatse" mobile application that allows remote control of Onkyo A/V receivers. Control volume and add custom commands.

It currently has a 4.6 rating out of 63 reviews on Google Play.

Frameworks

Spark, Flutter, OAuth 2

Tools

Git, Apache Airflow

Other

Mathematical Finance, Data Engineering, Machine Learning Operations (MLOps), GitOps, Monte Carlo Simulations

Languages

Rust, Python, Scala, SQL, C++, Java, Solidity

Libraries/APIs

React, Node.js, Spark ML, REST APIs

Paradigms

REST

Platforms

Linux, Kubernetes, Visual Studio Code (VS Code), Google Cloud Platform (GCP), Amazon Web Services (AWS), Android

2011 - 2013

Master's Degree in Mathematical Finance

University of North Carolina Charlotte - Charlotte, NC

Collaboration That Works

How to Work with Toptal

Toptal matches you directly with global industry experts from our network in hours—not weeks or months.

1

Share your needs

Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2

Choose your talent

Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3

Start your risk-free talent trial

Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.

Top talent is in high demand.

Start hiring