Data Scientist

Actively Reviewing the Applications

Hudson Data

India, Haryana, Gurugram Full-Time On-site

Posted 3 hours ago • Apply by June 14, 2026

Job Description

Company Description

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.

Role Overview

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.

Key Responsibilities

Model Development

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

Data Analysis & Feature Engineering

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

Machine Learning & AI Techniques

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

Model Validation & Deployment

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

Collaboration

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

Technical Skills

Machine Learning & Data Science

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

Programming

Python (required)
Experience with:

PyTorch or TensorFlow

Scikit-learn

Pandas / NumPy

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

Data Engineering & Analytics

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

Modeling Techniques

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

Education

MS or PhD in one of the following fields:

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

Why Join Hudson Data

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