Data Scientist (Fraud Detection)

• We are looking for a motivated Entry-Level Data Scientist to join our Fraud Detection team

• In this role, you will leverage your machine learning and data analysis skills to identify fraudulent activities, build predictive models, and uncover hidden patterns in large datasets

• You will work closely with cross-functional teams to develop scalable solutions that enhance our fraud detection capabilities

• This is a great opportunity to grow your skills in a fast-paced, data-driven environment while making a real impact in the fight against fraud

• Develop and deploy machine learning models for fraud detection and risk assessment

• Perform exploratory data analysis (EDA) to identify trends, anomalies, and patterns in transactional data

• Clean, preprocess, and analyze large datasets using Python and popular data science libraries (pandas, NumPy, scikit-learn, etc.)

• Collaborate with engineering and business teams to integrate ML models into production systems

• Continuously monitor model performance and refine algorithms to improve accuracy

• Stay updated with the latest advancements in fraud detection techniques and ML/AI technologies

Benefits

• Stock Options

• Commuter Passes

• Paid Time Off

• Catered Lunch and Dinner

• Team and Company Outings- Prior internship or project experience in fraud modeling, risk analysis, or related fields is a plus

• Strong problem-solving skills and patience for deep-dive data exploration

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related quantitative field. Ph.D. degree is a plus

• Solid understanding of machine learning algorithms (supervised/unsupervised learning, anomaly detection, classification, etc.)

• Experience with SQL and data manipulation/analysis in large datasets

• Strong programming skills in Python and familiarity with data science libraries (NumPy, Pandas, scikit-learn, TensorFlow/PyTorch is a plus)

• Excellent communication skills and ability to work in a collaborative

• Knowledge of graph-based fraud detection techniques

• Experience with cloud platforms (AWS, GCP, Azure)

• Familiarity with big data tools (Spark, Hadoop, Dask)