Senior Data Scientist - Enterprise AI for Finance

Tiger Analytics Inc.; Atlanta (GA); Vor Ort; USD 120.000 - 180.000; Vollzeit

Benefits dieser Stelle

Medical insurance

Dental insurance

Vision insurance

Retirement plan

Disability Insurance

Life Insurance

HSA/FSA (USA)

Discretionary time off

Zusammenfassung

Tiger Analytics, based in Atlanta, GA, is seeking a hands-on Senior Data Scientist to design, deploy, and scale AI/ML solutions for major Banking/Financial Services clients. The role focuses on translating financial challenges into robust data-driven models using Python and the broader ML stack.

You will work closely with enterprise stakeholders, implement end-to-end modeling pipelines, and collaborate with MLOps for production deployment.

Qualifikationen

• 6–10 years of experience in Data Science, Advanced Analytics, or ML roles.
• Experience in Banking/Financial Services domain (e.g., risk analytics, fraud prevention).
• Proficiency with Python and its scientific stack for end-to-end ML projects.

Aufgaben

• Design, build, validate, and optimize machine learning models for financial services clients.
• Write clean, production-ready Python code for data manipulation, feature engineering, model training, and evaluation.
• Partner with stakeholders to refine requirements and deliver data-driven solutions.
• Collaborate with MLOps and cloud engineering to productionize models and monitor performance.
• Present insights and methodologies clearly to technical and non-technical audiences.

Kenntnisse

Python

SQL

ML Techniques

Pandas

Scikit-Learn

XGBoost

TensorFlow

PyTorch

Cloud

Ausbildung

Bachelor's in CS/DS

Master's in CS/DS

Tools

XGBoost

TensorFlow

PyTorch

Jobbeschreibung

Tiger Analytics, based in Atlanta, GA, is seeking a hands-on Senior Data Scientist to design, deploy, and scale AI/ML solutions for major Banking/Financial Services clients. The role focuses on translating financial challenges into robust data-driven models using Python and the broader ML stack.

You will work closely with enterprise stakeholders, implement end-to-end modeling pipelines, and collaborate with MLOps for production deployment.