Senior Data Scientist - BFS

Tiger Analytics Inc.•Atlanta, GA

22d•Onsite

About The Position

Tiger Analytics is seeking a hands-on, highly skilled Senior Data Scientist to join their team in Atlanta, GA. This role involves working closely with enterprise clients in the Banking and Financial Services sector to design, develop, and deploy cutting-edge machine learning and predictive models. The Senior Data Scientist will be responsible for translating complex financial business challenges into robust AI/ML solutions and writing clean, scalable code. The company is at the forefront of the AI revolution, helping enterprises move beyond traditional analytics to embrace Generative AI, agentic AI, intelligent automation, and cloud-native data platforms.

Requirements

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

Responsibilities

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.
Write clean, modular, production-ready Python code to perform end-to-end data manipulation, feature engineering, model training, and performance evaluation.
Partner directly with business stakeholders, product managers, and engineering teams to understand business context, refine requirements, and deliver data-driven solutions.
Collaborate with MLOps and cloud engineering teams to transition models from prototype to scalable production environments, tracking model performance and drift over time.
Present analytical findings, business impact, and technical methodologies clearly to both technical teams and non-technical business leaders.