Location : Nashville, TN -Need locals only

Nature of Job : 3-4 days Onsite

Duration : 12+ months C2H

Interview Mode: 2-3 Virtual rounds

Create and refine quant algorithm scripts in Python to solve complex financial problems.

Close to a Data Science Engineer type of role.

Strong knowledge o f Python programming language and its frameworks, with a focus on back-end development.
Knowledge of software development principles and Object-Oriented development and design patterns ( C# experience a plus)
Experience with back-end technologies such as Oracle, SQL Server, .NET, REST API, microservices , etc.
Understanding of parallel programming concepts and packages – parallelization/multiprocessing/cloud .
Working experience/knowledge of Azure is a plus (CI/CD, Kubernetes, Data Factory, Containers/Pods/Docker, [ AWS is ok - Azure equivalent ]

Strong mathematical and statistical skills, with the ability to apply them to develop financial algorithms.
Experience in developing and optimizing quantitative algorithms for financial applications.
Strong analytical and problem-solving abilities, with a focus on developing innovative solutions to complex problems.
Ability to troubleshoot and optimize existing analytics solutions for improved performance.

Tax Advantaged Taxable Bond SMAs (differentiate our Taxable SMAs with an industry first systematic tax optimization for taxable bonds)
Capacity to develop additional Taxable SMA products (potentially offshore solutions, completion fund products, non-USD, etc)

Type of Quant/Algo/Analytic Software to Develop

Automated Transition Analysis (real-time transition from existing bonds to Client portfolios via Service Center)
Automated Proposals (cash funded proposals/model portfolios via Service Center)
Trade Rationales (unparalleled transparency for advisors into SMA trading activity and benefits)
Transition Scorecards (proactive touch point summarizing onboarding process and portfolio changes/benefits)

Pre-screening interview video (5-10 min long)

What is your experience with Pandas/DataFrame manipulation – what techniques do you use for data gathering/cleaning/storing?
Speak about your approach to Object-oriented programming / performance optimization and data structure selection in your coding.
Usage of NumPy / Statistical Analysis and any type of Linear Algebra experience (usage of matrices, optimization [minimize], interpolation).
What is your level of involvement in the SDLC beyond just writing code – QA, unit testing, deployment, scalability in PROD

Python Developer

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