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# Senior Data Scientist - BFS

- **Company:** Tiger Analytics
- **Location:** Atlanta, GA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cloud Engineering, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, NumPy, Tensorflow, SQL Databases, Unstructured Data, Data Processing, Feature Engineering, Pytorch, Pandas, Scikit Learn, Information Technology, Data Analytics, Xgboost, Machine Learning Operations, Multiaccess Edge Computing
- **Published:** October 15, 2026
- **Apply:** https://www.thejobnetwork.com/job/d02ef750-4441-46fd-b2ba-6c9501c57c83/senior-data-scientist-bfs

## About the Role

* Experience: 6-10 years of experience in Data Science, Advanced Analytics, or Machine Learning roles.
 * Domain Expertise: Proven track record working within the Banking or Financial Services industry (e.g., consumer banking, risk analytics, fraud prevention, wealth management, or credit modelling).
 * Technical Skills: Strong, hands-on proficiency in Python and its scientific ecosystem (e.g., Pandas, NumPy, Scikit-Learn, XGBoost, TensorFlow, or PyTorch).
 * Modelling Capabilities: Hands-on experience designing and building complex, cutting-edge AI/ML models across the entire development lifecycle.
 * Data Handling: Strong proficiency in SQL and experience working with large-scale, structured and unstructured datasets.
 * Education: Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field.

## Description

We are seeking a hands-on, highly skilled Senior Data Scientist to join our team in Atlanta, GA. In this role, you will work closely with enterprise clients in the Banking and Financial Services sector to design, develop, and deploy cutting-edge machine learning and predictive models. You will be responsible for translating complex financial business challenges into robust AI/ML solutions and writing clean, scalable code.

What you will be doing

 * Model Development & Innovation: Design, build, validate, and optimize cutting-edge machine learning and statistical models (e.g., predictive analytics, credit/risk modeling, fraud detection, customer segmentation) tailored for the financial sector.
 * Hands-on Execution: Write clean, modular, production-ready Python code to perform end-to-end data manipulation, feature engineering, model training, and performance evaluation.
 * Client & Stakeholder Engagement: Partner directly with business stakeholders, product managers, and engineering teams to understand business context, refine requirements, and deliver data-driven solutions.
 * Model Operations: Collaborate with MLOps and cloud engineering teams to transition models from prototype to scalable production environments, tracking model performance and drift over time.
 * Insights & Communication: Present analytical findings, business impact, and technical methodologies clearly to both technical teams and non-technical business leaders.