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Data Scientist (Fraud Detection)

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Onsite

Data Scientist (Fraud Detection)

DataVisor

Data Scientist (Fraud Detection)

Skills

Google Cloud PlatformSQL QueriesBig DataPythonData ScienceNumPypandasPyTorchTensorFlowData AnalysisMachine LearningAIAWSHadoopAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Mountain View, CA, USA

Work mode:

Onsite

Published on:

29 August 2026

What they need


  • 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)

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