Dive into cutting-edge R&D applying Agentic AI to telecommunications standards. This internship offers a unique chance to work on a platform designed to revolutionize how…
Data Science Intern – Financial Crime Analytics & AI
- Location
- India
- Posted
- Status
- Closed
About this role
Work on real-world challenges involving financial crime detection and AI-driven compliance solutions.
🎯 About the Role
This internship focuses on leveraging data science and AI to combat financial crime. You will analyze financial data, develop machine learning models, and contribute to innovative compliance technology.
🛠️ Key Responsibilities
- 📊 Analyze transactional and customer onboarding data to identify suspicious patterns
- 🤖 Develop machine learning models for risk scoring and anomaly detection
- 💡 Assist in building AI-powered features for transaction monitoring
- 🔍 Perform exploratory data analysis on large financial datasets
- 📈 Design and evaluate predictive models to improve alert quality
- 🖥️ Create dashboards and visualizations for compliance and risk intelligence
✅ Requirements & Skills
- 🎓 Pursuing or recently completed a degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field
- 🐍 Strong programming skills in Python
- 📚 Knowledge of Pandas, NumPy, Scikit-Learn, and data visualization libraries
- 🧠 Understanding of machine learning fundamentals and statistical analysis
- 🗄️ Familiarity with SQL and database concepts
🎁 Perks & Benefits
- 💼 Hands-on experience with real-world banking and financial crime datasets
- 🧑🏫 Mentorship from industry professionals
- 🚀 Opportunity to contribute to production-grade analytics
- 🏢 Potential conversion to a full-time role based on performance
💰 Stipend: Type: Stipend
⏳ Duration: 3 - 6 Months
📍 Location: Remote (India) / Bengaluru / Pune
📩 How to Apply
If you are unable to apply on LinkedIn, please share your resume at deepesh@transvision.us with subject line: "Application – Data Science Intern"
How to apply
Applications are handled on in.linkedin.com, not here. The link below opens the original posting.
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