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Mid-level generalist ML role, common skillset, and likely metro hiring increases candidate competition.
Core ML, Python, and Databricks skills are transferable, but financial model governance adds sector specificity.
Explicit 2–5 years plus many mandatory ML, Spark/Databricks, cloud, and governance requirements.
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Develop and implement predictive models and advanced analytics solutions to extract actionable business insights under supervision.
Ensure data integrity, conduct exploratory data analysis, feature engineering, model training, validation, and support implementation and monitoring of models.
Collaborate with stakeholders to communicate results, maintain regulatory compliance in model development, and contribute to data science innovation and model risk management processes.
Bachelor’s degree in Statistics, Mathematics, Engineering, Data Science, Economics, Computer Science, or related quantitative field.
2 to 5 years of relevant work experience in data science, predictive modeling, and data mining.
Proficiency in Python, SQL (including Spark SQL), machine learning algorithms (bagging, boosting, neural nets), and experience with Databricks, Spark, cloud platforms (AWS/Azure), and version control (Git).
Must comply with model governance and regulatory requirements; work is performed in an office environment.
Experience working in large-scale data and distributed processing environments with production ML systems.
Demonstrated ability to translate complex model results into actionable business recommendations and communicate effectively with stakeholders.
Familiarity with emerging technologies such as GenAI, NLP, time series forecasting, model monitoring, and advanced ML experimentation and evaluation tools.