AML analytics Intern

Solytics Infotech; Pune District; On-site; Full time

Job description

Solytics Partners is a Global Analytics firm, recognized with multiple industry awards for innovation and excellence. Our team comprises experts with deep domain knowledge in risk, analytics, AI/ML, AML/FCC, and fraud. By converging this expertise with cutting‑edge technologies like AI, Machine Learning, Generative AI, and Large Language Models (LLMs), we deliver powerful automated platforms and incisive point solutions.

Our offerings enable clients to streamline and future‑proof their risk, AML, and analytics processes, comply seamlessly with global regulations, and safeguard financial systems. Whether it’s solving complex challenges or driving operational efficiency, Solytics Partners is committed to empowering organizations with transformative tools to stay ahead in an evolving regulatory landscape.

Job Summary:

We are seeking a motivated and enthusiastic AML Analytics Intern to join our analytics team. The ideal candidate should have a strong foundation in Data Science, Data Analytics, Python, and Machine Learning, with an interest in Anti‑Money Laundering (AML) and Fraud Detection. This role provides an opportunity to work on real‑world data analytics and machine learning projects focused on identifying suspicious patterns, detecting fraudulent activities, and supporting AML and financial crime risk management initiatives.

Key Responsibilities:

• Assist in analyzing large datasets to identify patterns, anomalies, and trends related to AML and fraud detection.
• Support the development and implementation of analytical and machine learning models to identify suspicious and potentially fraudulent activities.
• Use Python and relevant data science libraries to clean, preprocess, analyze, and transform datasets.
• Assist in developing predictive and statistical models for fraud detection, AML monitoring, and risk analytics.
• Perform exploratory data analysis and feature engineering to identify meaningful insights from financial and transactional data.
• Support model testing, validation, performance evaluation, and error analysis.
• Research AML and fraud typologies to understand patterns and indicators associated with financial crime.
• Collaborate with data scientists, analysts, and domain experts to understand business requirements and contribute to analytical solutions.
• Prepare data visualizations, reports, and documentation to communicate analytical findings and insights.

Key Requirements:

• Pursuing or recently completed a Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
• Strong foundation in Python and data analysis.
• Basic understanding of Machine Learning, Statistics, and Data Science concepts.
• Familiarity with Python libraries such as Pandas, NumPy, and Scikit‑learn.
• Understanding of machine learning algorithms such as classification, regression, clustering, and anomaly detection is preferred.
• Basic knowledge of Data Analytics, Exploratory Data Analysis, and Data Preprocessing.
• Interest or basic understanding of AML, Fraud Detection, Financial Crime, or Risk Analytics.
• Knowledge of SQL and data visualization tools such as Matplotlib, Seaborn, or Power BI is an added advantage.
• Strong analytical and problem‑solving skills with an eagerness to learn and work on real‑world business problems.
• Good communication skills and ability to work effectively in a collaborative team environment.
• Prior academic or projects related to Data Science, Machine Learning, Fraud Detection, or AML Analytics will be an added advantage.