We are looking for an experienced Data Scientist / Machine Learning Engineer to join our AI/ML team in Mumbai.
The ideal candidate will have a strong background in building machine learning and deep learning models particularly in fraud detection, transaction monitoring, or risk analytics and will be responsible for the end-to-end model lifecycle, from data exploration to production deployment and monitoring.
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
Mandatory 5 years of experience as Data Scientist / ML Engineer
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, GCN).
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
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.
