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Job Summary

Under supervision and guidance, the Data Scientist, 2 applies data science in advanced analytics such as predictive modeling and data mining in a big data environment in order to deliver predictive models and insights. The Data Scientist, 2 produces insights that encompass the different product types and channels and maintain regulatory standards. The Data Scientist, 2 assists with ensuring accurate and timely implementation of solutions, quantifying the overall financial impact to the business, and communicating results.

Essential Job Functions:

Analytics under supervision and guidance, completes the following:

1) extracts and samples data, conducts data integrity checks and applicable data pre-processing such as treatment of missing values and outliers
2) conducts exploratory data analysis for preliminary data insights to drive the selection of modeling approach that best addresses the business problem
3) reveals hidden data patterns by data mining using unsupervised learning techniques such as clustering analysis and factor analysis
4) conducts feature engineering to create/derive model predictors with strong predictive power
5) trains/tunes classification/regression models by applying supervised learning techniques such as generalized linear models assuming applicable underlying distributions such as logit and gamma, tree-based models such as decision trees, random forest, boosted trees, etc., and neural net models
6) conducts proper model test/validation, diagnoses and fixes model issues (e.g., over-fitting) when applicable. Sizes the impact of using the models in production as part of the current strategy. Presents results and business case to manager. Provides support for implementation and monitoring of solutions that are implemented.

Collaboration under supervision and guidance, translates analytical results into useful recommendations for review with manager. Demonstrates strong verbal and written communication skills when working with internal partners and when presenting results to various audiences. Develops foundational knowledge of credit card operations, banking, financial, loyalty rewards, retail, and credit card regulations while working with the business. Collaborates with other data scientists in the company to share best practices and data science innovations.

Data Science innovation with direction from leader, researches industry trends in data science of new tools, emerging algorithms, advanced platforms, and alternative data to enhance modeling effectiveness and efficiency. Conducts use case testing for new tools/techniques/platforms/data and provides user input/feedback.

Model Risk Management - develops foundational knowledge on common model risks and related regulatory requirements; applies proper 1st line of defense controls during model development process to minimize model risk; creates comprehensive model governance documentation and archives model data, scripts, and results; collaborates with model risk management partners to complete model validation/auditing; completes remediation as required by model governance process.

Reports To:

Manager or higher

Direct Reports:

None

Working Conditions/ Physical Requirements:

Normal Office Environment.

Minimum Qualifications:

Education Required: Bachelor s Degree in Statistics, Mathematics, Engineering, Data Science, Economics, Computer Science, or another quantitative field
2 to 5 years of related work experience

Preferred Qualifications:

Education: Master s Degree, PhD in Statistics, Mathematics, Engineering, Data Science, Economics, Computer Science, or another quantitative field
3 or more years of professional hands-on experience in developing statistical models/machine learning models and conducting data mining to solve business problems. Experience in extracting and processing large files/data sets. Experience interpreting model results and translating insights into business recommendations.
Experience interpreting model results and translating insights into business recommendations.
Strong machine learning fundamentals, statistics and model evaluation.

Knowledge, Skills and Abilities:
Must:

Python
Spark SQL
Statistical Concepts
Structured Query Language (SQL)
Python, SQL
ML algorithms (Bagging, Boosting, Neural Nets)
Databricks Spark
Git / version control
ML experimentation and evaluation tools
Data visualization and reporting tools
Cloud computing - AWS and/or Azure
Data Analytics
Data Science
Machine Learning
Pivot Tables
PowerPoint Presentations
Predictive Modeling

Good to Have :

Financial Services
Natural Language Processing (NLP)
Time Series Forecasting
Cloud platforms (Azure / AWS / GCP)
ML experimentation and monitoring tools
GenAI tooling (LLM frameworks, embeddings, vector stores)
Advanced Python and SQL for data science and modeling.
Experience with large scale data and distributed processing (Databricks / Spark like environments).
Experience working with production ML systems.
Working understanding of GenAI fundamentals, including Transformers, LLMs, embeddings, and prompting concepts.
Strong problem solving and stakeholder communication skills.
Model monitoring, drift detection, and retraining workflows.
Optimization, interpretability (SHAP, feature importance)

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

Data Scientist 2

Job Description

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