FS Investments is looking for a motivated Data Scientist to join our Data Strategy team. The selected candidate will be a member of the Data Strategy team, which is responsible for supporting the firm’s Investment Management, Distribution & Marketing organizations. This role will have a focus on analytical modeling, working with key business stakeholders to derive value from various data sources.

Data Strategy is an interdisciplinary team that spans FS Investments. Made up of data science, analytics, and business domain experts, we are at the forefront of implementing data-driven strategies across the organization. We focus on delivering the platforms, analytical solutions and services that enhance User productivity and maximize business value.

**This is an on-site role with a minimum of 4 days in-office. Must be able to commute within the greater Philadelphia area. 

The successful candidate is expected to:

• Work as an integral part of the Data Strategy team, by learning to leverage our platform to perform analysis and derive key insights from our data.
• Use statistical and machine learning (ML) techniques to create robust investment and distribution solutions.
• Develop skills and participate in training to become proficient with our data science platform.
• Participate in the evolution of the strategic vision for ML/AI at FS Investments.
• Research and implement relevant ML and statistical approaches.
• Contribute to the development and improvement of existing codebases, pipelines, and models.
• Document code, algorithms, and processes for effective knowledge sharing and reproducibility.
• Stay connected and current with the latest developments in data science and present findings to team members.
• Work closely with key business stakeholders, data scientists, and analytics professionals to understand project requirements, develop ML models and algorithms, and contribute to the translation of prototypes into production systems.

Qualifications:

• Bachelor’s degree in math, physics, logic, philosophy, or a related scientific discipline with a minimum of 2-4 years of practical experience in data science; an advanced degree in data science is a plus.
• Basic understanding of data cleaning, feature engineering, model development and evaluation.
• Familiarity with common ML frameworks and libraries, such as TensorFlow/Pytorch, Scikit-learn, Numpy, Pandas, etc.
• Solid programming skills in Python and the ability to write clean, efficient, and well-documented code.
• Experience working in an Agile environment.
• Self-motivated team-player, good communicator, embraces challenges, and keen to learn.
• Preferred understanding of ML methods and Statistics, including MLOps lifecycle and associated challenges at each stage of development.
• Preferred knowledge and hands on experience with deep learning architectures and frameworks.
• Passionate about learning new AI/ML skills and staying updated with the latest developments in the field.