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Metro location, popular Data Scientist title, and financial-brand increase applicant competition.
Financial services and actuarial experience preferred, reducing transferability across industries.
Explicit 7–10 years plus required ML, PySpark/Databricks and MLOps skills increase filter strictness.
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Develop, validate, and tune predictive models using machine learning techniques to solve business problems with measurable outcomes.
Handle large and complex datasets including feature engineering and data preparation using Python, SQL, and Spark environments.
Lead and review data science work, ensure model interpretability and documentation in an audit-aware environment, and guide junior team members.
7 to 10 years of relevant work experience in data science or related field.
Strong hands-on skills in predictive modeling and machine learning including supervised and unsupervised techniques.
Proficiency in Python, SQL, and PySpark/Databricks for data manipulation and machine learning.
Numerical based degree or postgraduate qualification.
Experienced in linking model outputs directly to business decisions and insights within a financial services or similar domain.
Familiar with MLOps concepts including model deployment and lifecycle management, along with GitHub and CI/CD processes.
Capable of working in regulated, audit-aware environments with clear documentation and stakeholder communication.