Senior Data Scientist – BFS

Tiger Analytics

. Design, build, validate, and optimize machine learning and statistical models for predictive analytics, credit/risk modeling, fraud detection, and customer segmentation in the financial sector .

Posted 10/15/2026full-timeAtlanta • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and optimizing machine learning models for predictive analytics in the financial sector, with strong proficiency in Python and SQL. Capable of collaborating with cross-functional teams to deliver data-driven solutions and lead AI adoption initiatives.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProgrammingSQL ProficiencyFinancial Services ExperienceData Science Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPredictive AnalyticsStatistical ModelingFeature EngineeringModel TrainingPerformance EvaluationData ManipulationAI/ML Model DesignData AnalysisData Visualization

Soft Skills

CollaborationCommunicationStakeholder EngagementStrategic ThinkingProblem Solving

Tools & Technologies

PandasNumPyScikit-LearnXGBoostTensorFlowPyTorchMLOpsCloud Engineering

Industry Keywords

BankingFinancial ServicesRisk AnalyticsFraud PreventionCredit ModelingConsumer BankingWealth Management

Tech Stack

Tools & technologies

CloudNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Design, build, validate, and optimize machine learning and statistical models for predictive analytics, credit/risk modeling, fraud detection, and customer segmentation in the financial sector
Write clean, modular, production-ready Python code for end-to-end data manipulation, feature engineering, model training, and performance evaluation
Partner 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
Track model performance and drift over time
Present analytical findings, business impact, and technical methodologies to technical teams and non-technical business leaders
Lead strategic engagements delivering enterprise-wide AI adoption, cloud-native data modernisation, and measurable business outcomes
Collaborate with AI, data, engineering, and consulting talent across geographies to deliver transformative solutions and grow strategic client partnerships

Requirements

What you’ll need

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, including consumer banking, risk analytics, fraud prevention, wealth management, or credit modelling
Strong, hands-on proficiency in Python and its scientific ecosystem, including 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
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

Benefits

Comp & perks

Medical insurance
Dental insurance
Vision insurance
Retirement accounts
Long-term disability
Short-term disability
Life insurance
HSA/FSA accounts (USA)
Discretionary time off
Continuous learning and AI upskilling
Hands-on exposure to emerging technologies
Opportunities to build expertise in Generative AI, agentic AI, and next-generation data platforms
Significant career development opportunities
High degree of individual responsibility
Equal employment opportunities