# Data Scientist at Morgan Stanley

Morgan Stanley is hiring a Data Scientist in Budapest, Hungary. Level rates it Little AI ●○○○; you can [apply on Morgan Stanley](https://jobsbylevel.com/go/7f85214c-306f-4ac8-9e98-5455437b9ded?utm_medium=ai-agent).

AI Level 1, AI centrality 15 out of 100. Budapest, Hungary.

## Details

- Company: [Morgan Stanley](https://jobsbylevel.com/companies/morgan-stanley)
- AI level: AI Level 1 (score 15 out of 100)
- Location: Budapest, Hungary
- Posted: November 5, 2026
- Apply: https://jobsbylevel.com/go/7f85214c-306f-4ac8-9e98-5455437b9ded?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=data-scientist-at-morgan-stanley-7bb81e

## Description

We are seeking a Data Scientist to join the CDRR Technology team within the Fraud Department. The position is responsible for the development of statistical and machine learning models used to identify, assess, and mitigate fraud risk across Morgan Stanley products. In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities. This is a Data Scientist position at Director level, which is part of the job family responsible for providing specialist data analysis and expertise that drive decision-making and business insights as well as crafting data pipelines, implementing data models, and optimizing data processes for improved data accuracy and accessibility, including applying machine learning and AI-based techniques. Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world. What You’ll Do in the Role: Independently execute end-to-end model development, including technical documentation. Develop and evaluate models for highly imbalanced, non-stationary, and adversarial environments where fraud patterns, customer behaviour, and operational processes evolve over time. Apply statistical and mathematical principles to model selection, validation, and performance assessment, including the treatment of class imbalance, overfitting, data leakage, missing data, and model stability. Monitor deployed models, diagnose performance degradation, assess emerging fraud patterns, and recommend recalibration, redevelopment, threshold changes, or retirement as appropriate. Provide technical guidance and mentoring to junior Data Scientists and Data Engineers. Perform exploratory data analysis and construct analytical datasets from large, complex, and imperfect real-world data. Conduct feature engineering and select appropriate supervised and unsupervised machine learning techniques based on the modelling problem and available data. Train, test, and evaluate machine learning models using appropriate statistical methodologies and performance metrics. Manage assigned model development projects independently, with guidance from senior members of the team. Partner with Data Scientists, Data Engineers, and other stakeholders to support the development and implementation of robust, scalable modelling solutions. What you’ll bring to the role: 6+ years of professional experience in data science, machine learning, statistical modeling, or quantitative analytics. Demonstrated depth of knowledge in statistical inference, probability, sampling, hypothesis testing, regularization, bias-variance trade-offs, optimization, feature selection, dimensionality reduction, model calibration, and statistical diagnostics. Hands-on experience performing exploratory data analysis, constructing analytical datasets, and engineering features from large, complex, and imperfect real-world data. Advanced proficiency in Python and SQL, and experience with Hadoop, Hive, Impala, Spark, or PySpark. Experience designing statistically valid training, validation, and testing approaches and evaluating models using appropriate performance, calibration, stability, and diagnostic measures. Demonstrated ability to independently manage model development assignments and produce technical documentation suitable for review and governance. Excellent written and verbal communication skills. Including the ability to communicate complex analytical methods and results to technical and non-technical stakeholders. Good to have: Experience developing machine learning or statistical models for fraud detection, financial crime, transaction monitoring, payment risk, or anomalous-behavior detection. Experience analyzing high-volume transactional, account, client, or behavioral data within financial services. Knowledge

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/data-scientist-at-morgan-stanley-7bb81e

Source: https://jobsbylevel.com/jobs/data-scientist-at-morgan-stanley-7bb81e

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