Data Scientist - Machine Learning

Job Title :

Location

Experience : 4- 7 years

About The Role

Key Responsibilities

• Design and develop ML/DL models for fraud detection, risk scoring, and transaction monitoring.
• Experiment with supervised, unsupervised, and semi-supervised learning techniques for anomaly detection.
• Manage data pre-processing, feature engineering, training, validation, deployment, and continuous improvement.
• Implement scalable and reproducible ML pipelines.
• Deploy and maintain models in production using MLOps frameworks such as MLflow, Kubeflow, Airflow, or AWS Sagemaker.
• Implement CI/CD for model updates and retraining.
• Partner with data engineering teams to build robust data pipelines, feature stores, and real-time scoring infrastructure.
• Build systems for automated model evaluation, drift detection, and performance reporting.
• Work closely with product, compliance, and risk teams to define fraud detection strategies and translate business needs into ML solutions.
  

Required Skills & Qualifications

• Bachelors or Masters degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field.
• Proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch).
• Experience with fraud detection, transaction monitoring, or anomaly detection.
• Strong background in machine learning and deep learning architectures (RNN, LSTM, Transformer, GNN).
• Experience with MLOps tools MLflow, Kubeflow, Airflow, or AWS Sagemaker.
• Familiarity with data pipelines and distributed systems (Spark, Kafka, etc.).
• Experience deploying ML models on AWS / GCP / Azure environments.
  

Soft Skills

• Analytical and structured problem-solving ability.
• Strong communication skills to collaborate with technical and business teams.
• Ability to work independently and drive results in a fast-paced environment.
  

Preferred Experience

• Hands-on experience with real-time fraud detection or behavioral anomaly detection.
• Exposure to financial transactions, payment gateways, or card network ecosystems.
• Understanding of explainable AI (XAI) tools such as SHAP, LIME, or Captum.
• Familiarity with graph-based fraud detection approaches.
  

What We Offer

• Opportunity to build next-generation fraud detection systems at scale.
• Collaborative and high-growth work environment.
• Competitive compensation and benefits.
• Chance to work on real-world AI/ML challenges in the fintech domain from our Mumbai on-site office.