Senior Quantitative Developer

Key Skills

Aartificial intelligence, Data science techniques, Deep Learning, Large Language Models - LLMs, Machine learning techniques, Natural Language Processing (NLP), PLSQL, Python Programming, R Programming

Job Description

Develops quantitative software applications and full-stack solutions using technical tools — R, Python, PL/SQL databases, and Artificial Intelligence/Machine Learning (AI/ML) toolkits. Performs analytics and builds quantitative process frameworks to support investment needs and develop new solutions. Executes probability, linear regression, time series data analysis and data science models. Designs and develops new quantitative models and products for equities, fixed income, and alternative asset classes. Ensures repeatability on projects through software development standard methodologies. Collaborates with multiple partners, includes fundamental and quantitative researchers, technology partners and senior management. Develops original and creative technical solutions to support on-going development efforts.

Primary Responsibilities:

• Develops original and creative technical solutions to support on-going development efforts.
• Designs applications or subsystems on major projects involving multiple platforms and supports a range of divisional initiatives.
• Supports and performs all phases of tests leading to implementation.
• Establishes project plans for projects of moderate scope.
• Performs independent and complex technical and functional analysis for multiple simultaneous projects.
• Analyzes information to determine, recommend, and plan computer software specifications on major projects and proposes modifications and improvements based on user need.
• Develops software system tests and validation procedures, programs, and documentation.

Education and Experience:

Bachelor’s degree (or foreign education equivalent) in Computer Science, Computational Finance, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and three (3) years of experience as a Senior Quantitative Developer (or closely related occupation) developing software applications and full-stack solutions for financial investments using R, Python, PL/SQL databases, or AI/ML techniques.

Or, alternatively, Master’s degree (or foreign education equivalent) in Computer Science, Computational Finance, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and one (1) year of experience as a Senior Quantitative Developer (or closely related occupation) developing software applications and full-stack solutions for financial investments using R, Python, PL/SQL databases, or AI/ML techniques.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) developing systems to build quantitative models for systematic financial investments using R and Python; developing time series forecasting models, multi-asset class portfolio construction strategies, risk management tools and alpha research to build investment strategies, using R, Python, MSCI Barra and Morningstar; and building automated diagnostic reporting processes for model risk management using R and Python.
• DE prototyping and deploying large scale data science projects, and AI/ML models and interfaces using Spark, Tensorflow, Python data science libraries, and Deep Learning (DL) frameworks on Amazon Web Services (AWS) and On-premise computing environments; and developing investment tools and strategies using Natural Language Processing (NLP), Large Language Models (LLM), Neural Networks, and Supervised and Unsupervised Learning algorithms.
• DE building data analytics life cycle for internal and vendor-based financial markets data, using Python, Autosys and PL/SQL databases; developing complex mechanisms and performance tuned PL/SQL queries to extract data from databases, APIs, and build processes (for data transformation, standardization, cleansing, and aggregation) using Python and Informatica; building algorithms for large scale data processing and investment risk calculations using distributed computing and parallel processing techniques; and building dashboards for alpha and beta performance analysis using Python Dash, R Shiny, and Tableau.
• DE building highly scalable production-ready code complying with software engineering practices using Kubernetes and Docker systems; performing Continuous Integration/Continuous Deployment (CI/CD) (using Linux and Jenkins), code versioning (using Github), batch scheduling (using Autosys and Airflow), and REST APIs (using FastAPI and Flask); and creating executables using AWS Lambda, S3, and EC2.