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Mid-level Data Scientist in a metro office with a common title and modest brand, moderate applicant competition.
Core ML engineering skills are transferable, though finance domain experience is preferred.
Explicit 6+ years and mandatory ML/Python skills create strict filtering.
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Develop and maintain Python software to acquire, clean, and prepare datasets for machine learning model training and validation.
Design, tune, and evaluate machine learning algorithms including ensemble methods across supervised, unsupervised, and time series domains.
Collaborate with domain experts and cross-functional teams to analyze data, ensure quality, and present findings internally and externally, potentially guiding implementation teams.
6+ years of overall professional experience.
Undergraduate or graduate degree in Computer Science with a strong statistical background.
Proficiency in Python and common libraries (numpy, pandas, sklearn); experience with Linux and development tools.
Experience with advanced ML frameworks (e.g., Keras, TensorFlow) and Azure cloud preferred but not mandatory.
Experienced in end-to-end data science project lifecycle spanning hypothesis formation to production prototype development.
Track record of innovating beyond commercial solutions to enhance client and internal partner experiences in financial services or related sectors.
Capable of working with distributed ML training and inference, and contributing to team knowledge through mentoring and training activities.