Source description
About the role
3+ years of hands-on data science or applied ML experience, including at least one model you took from problem framing through production.
Strong proficiency in Python (NumPy, pandas, scikit-learn) and SQL.
Solid grounding in statistics, probability, and core machine learning methods.
Experience working with large-scale datasets on cloud platforms (AWS preferred).
Sound engineering judgment and the ability to work independently within a collaborative team.
Strong communication skills and a track record of working effectively across functions.
Experience in fintech or consumer finance, especially with regulated problems like credit or fraud.
Nice to have:
Familiarity with MLOps tooling such as MLflow and Docker.
Experience with BI tools such as Looker or Tableau.
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