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Mid-level ML role at a known global firm in a metro with broad skill requirements.
ML engineering skills are transferable, but finance experience preferred, creating moderate background sensitivity.
Requires 6+ years, CS degree, and concrete ML/engineering skills, making filters moderately strict.
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Develop software in Python to acquire, clean, and integrate data from multiple sources into datasets for model training and testing.
Build and tune machine learning models including supervised, unsupervised, and time series algorithms to optimize out-of-sample performance.
Maintain data quality through unit tests and data pipelines; present findings to internal and external stakeholders, and provide informal guidance on prototype engineering and data science practices.
6+ years of overall work experience.
Strong Python programming skills including libraries like numpy, pandas, and sklearn; experience with Linux environments and source control tools.
Bachelor's or graduate degree in Computer Science with strong statistical background.
Preferred but not mandatory: experience with advanced ML frameworks (Keras, TensorFlow), Azure cloud infrastructure; Finance sector experience or related coursework.
Practitioner knowledgeable across full data science lifecycle from hypothesis formation to data acquisition, model development, and presentation.
Experienced in implementing machine learning models with strong data wrangling and quality assurance capabilities.
Able to collaborate across teams, provide informal mentorship, and integrate new techniques from external partners.