Back
Popular mid-level ML/Data Scientist title with broad toolset and moderate-brand increases applicant density.
Core ML and data skills are transferable, but finance-specific model governance and regulatory context raise sensitivity.
Explicit 2–5 year requirement plus mandatory ML, Spark/Databricks, cloud, and regulatory model governance skills.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop, validate, and implement predictive models and data mining solutions in a big data environment under supervision.
Conduct data preprocessing, exploratory analysis, feature engineering, and apply machine learning algorithms to solve business problems.
Support model governance, risk management, and collaborate with stakeholders to translate analytical results into actionable business recommendations.
Bachelor’s degree in Statistics, Mathematics, Engineering, Data Science, Economics, Computer Science, or related quantitative field.
2 to 5 years of related work experience in data science, statistical modeling, and data mining.
Proficiency in Python, SQL, Spark SQL, machine learning algorithms, Databricks/Spark, cloud platforms (AWS/Azure), and version control (Git).
Work Environment: Normal Office Environment; direct reports: none.
Experience working with large-scale data and distributed processing environments with production ML systems.
Familiarity with advanced data science topics including GenAI fundamentals, interpretability techniques (e.g., SHAP), model monitoring and drift detection.
Ability to communicate modeling insights effectively to stakeholders and collaborate under guidance to apply data science innovations in financial services domain.