Data Scientist

Location: Gurgaon
Notice Period: Immediate joiners preferred (within 15 days)
Domain: Financial Crime & Fraud Analytics

Role Overview
We are seeking a highly skilled Data Scientist with strong expertise in fraud detection and financial crime analytics. The ideal candidate will be responsible for delivering end-to-end machine learning solutions, from problem formulation to model deployment, to detect anomalies, prevent fraud, and strengthen risk management frameworks.

Key Responsibilities
• Develop and implement machine learning models for fraud detection, risk scoring, and anomaly detection
• Work on end-to-end ML lifecycle including data collection, feature engineering, model training, validation, and deployment
• Apply advanced algorithms such as XGBoost, Random Forest, and other ensemble models for predictive analytics
• Analyze large and complex datasets to identify fraud patterns, suspicious behaviors, and emerging risks
• Collaborate with business, risk, and compliance teams to translate requirements into scalable data science solutions
• Deploy models into production and monitor performance, ensuring accuracy and stability over time
• Perform model tuning, validation, and performance optimization
• Build reusable, scalable ML pipelines and frameworks
• Work with structured and unstructured data sources to enhance model effectiveness
• Communicate insights and findings clearly to both technical and non-technical stakeholders

Required Skills & Qualifications
• 6–9 years of experience in Data Science / Machine Learning roles
• Strong proficiency in Python programming (NumPy, Pandas, Scikit-Learn, etc.)
• Hands-on experience with:
o Machine Learning algorithms (XGBoost, Random Forest, Gradient Boosting, etc.)
o Fraud Detection / Financial Crime Analytics use cases
• Experience in end-to-end ML project delivery
• Strong expertise in model development, evaluation, and deployment
• Solid understanding of statistics, probability, and data modeling techniques
• Experience working with large-scale datasets
• Knowledge of data preprocessing, feature engineering, and model explainability
• Strong analytical thinking and problem-solving skills

Preferred Qualifications
• Experience in Banking / Financial Services / FinTech domain, especially fraud and risk
• Familiarity with real-time fraud detection systems
• Exposure to big data technologies (Spark, Hadoop)
• Experience with model deployment tools (Docker, APIs, MLOps frameworks)
• Knowledge of regulatory compliance and risk frameworks in financial services