Data Scientist

Hudson Data specializes in building agile AI and machine learning solutions that power intelligent decision-making in financial services. We partner with organizations to design, implement, and scale advanced analytics and AI-driven risk systems that solve complex real-world problems.

Our work focuses on credit risk modeling, fraud detection, behavioral analytics, and decision intelligence platforms. By combining deep domain expertise with cutting-edge machine learning techniques, Hudson Data helps organizations transform how they evaluate risk, optimize portfolios, and detect emerging threats.

Our mission is to deliver innovative, data-driven solutions that improve financial decision-making at scale.

Hudson Data is seeking a Data Scientist specializing in Machine Learning and Credit Risk Modeling to develop next-generation predictive models used in underwriting, fraud detection, and portfolio risk management.

This role involves working with large-scale financial datasets, including credit bureau data, transactional behavior, and identity signals, to build advanced models using modern machine learning and deep learning techniques.

The ideal candidate is passionate about applying AI, sequence modeling, anomaly detection, and advanced feature engineering to solve challenging problems in risk analytics.

• Build predictive models for credit risk, fraud detection, and behavioral analytics
• Develop probability of default (PD), risk scoring, and portfolio performance models
• Design deep learning architectures for sequential and temporal data, including:

Transformer-based models

Temporal neural networks

Sequence mining techniques

• Apply anomaly detection techniques to identify suspicious patterns and emerging risk signals

• Analyze large-scale financial datasets including:

Credit bureau tradelines

Transaction and payment history data

Device and identity signals

• Develop advanced features using:

Time-series modeling

Behavioral patterns

Graph and network relationships

• Build scalable feature pipelines for model training and real-time scoring

Apply advanced ML techniques including:

• Gradient boosting models (XGBoost, LightGBM)
• Deep learning frameworks (PyTorch, TensorFlow)
• Transformer-based architectures
• Graph embeddings and network analytics
• Unsupervised learning and anomaly detection methods such as:

Isolation Forest

Autoencoders

Density-based detection

• Evaluate models using industry-standard metrics such as AUC, KS, Gini, lift, calibration, and stability
• Implement model monitoring, performance tracking, and drift detection
• Deploy models into production environments supporting real-time decision systems

• Partner with risk strategy, product, underwriting, and engineering teams
• Translate model insights into actionable credit strategies and policies
• Ensure models meet regulatory, compliance, and audit standards

• Strong expertise in supervised and unsupervised learning
• Experience with deep learning models for tabular and sequential data
• Familiarity with transformers and attention-based architectures

• Python (required)
• Experience with:

PyTorch or TensorFlow

Scikit-learn

Pandas / NumPy

Additional experience with Scala, Java, or C/C++ is a plus.

• Strong SQL skills and experience working with large datasets
• Experience with distributed data platforms such as:

BigQuery

Spark

Distributed processing frameworks

• Experience building feature engineering pipelines and model experimentation frameworks

Experience in several of the following areas:

• Credit risk modeling (PD, LGD, risk scoring)
• Sequence mining and temporal modeling
• Graph analytics and network intelligence
• Anomaly detection
• Ensemble modeling techniques

MS or PhD in one of the following fields:

• Data Science
• Computer Science
• Statistics
• Mathematics
• Operations Research
• Economics or other quantitative disciplines

At Hudson Data, you will work on cutting-edge machine learning systems used to power real-world financial decisions. Our team focuses on pushing the boundaries of AI-driven risk modeling, behavioral analytics, and decision intelligence.

You will have the opportunity to:

• Work on high-impact problems in fintech and risk analytics
• Build advanced ML systems using modern AI techniques
• Collaborate with a team passionate about innovation, research, and practical impact