Data Scientist - Sr Associate
On-siteBengaluru, Karnataka, India
Bengaluru, Karnataka, IndiaOn-siteFull TimeSenior LevelDoctorate Or Professional DegreeFinancial ServicesEnterprise
Full TimeSenior LevelDoctorate Or Professional DegreeEnterpriseFinancial Services
Job Summary
Develop, train, and deploy machine learning models for fraud prevention and risk management using AWS, Databricks, and PySpark. Research and implement novel architectures including Graph Networks, Agentic AI, and Large Language Models to build scalable, reusable solutions. Lead technical strategy within the team while mentoring junior members and collaborating with cross-functional groups to align modeling with business objectives. Monitor model performance in real-world environments to adapt to evolving fraud patterns and ensure reliability.
Required Qualifications
- Master's degree in Computer Science, Mathematics, Statistics, Economics, or a related quantitative field, or equivalent work experience
- Minimal 5-year of experience in developing and managing predictive risk models in financial institutions
- Deep understanding of machine learning theory and algorithms, with hands-on experience in both classical and deep learning methods
- Proficient in Python, SQL or PySpark with experience in deep learning frameworks such as PyTorch or TensorFlow, and classical machine learning tools like XGBoost or Scikit-learn
- Experience working with large datasets and building data pipelines using Databricks, PySpark, or similar technologies
- Experience working in AWS cloud environments
- Ability to build and test AI agents, iterate designs, and conduct rigorous testing for reliability and effectiveness
- Experience mentoring or coaching junior team members
Desired Qualifications
- Knowledge of graph analytics including GSQL will be an added bonus
- Experience or strong interest in Graph Analytics and Agentic AI
- Knowledge of GSQL
- Deep technical understanding of the mathematics behind algorithms, not just library usage
- Product-first mindset, with a focus on the role models play in the user experience and overall product responsibility
- Versatility in handling both tabular and non-tabular data using classical machine learning (e.g., trees/forests) and modern deep learning techniques
- Driven by impact and energized by the responsibility of having your models make decisions on live financial transactions
- Demonstrated ability to build scalable, reusable solutions that contribute to firmwide capabilities and long-term strategic goals
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