Morgan Stanley
From Morgan Stanley's careers page
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We are seeking a Data Scientist to join the CDRR Technology team within the Fraud Department.
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.
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.
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.
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 of fraud typologies, risk indicators, and security issues applicable to banking or Wealth Management.
Experience developing models within a regulated environment subject to Model Risk Management, validation, documentation, and governance requirements. Experience with data-visualization and reporting tools such as Tableau.
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At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work.
To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.
If this role is deemed a Certified role and may require the role holder to hold mandatory regulatory qualifications or the minimum qualifications to meet internal company benchmarks.
Flexible work statement
Interested in flexible working opportunities? Morgan Stanley empowers employees to have greater freedom of choice through flexible working arrangements. Speak to our recruitment team to find out more.
Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents.
Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.
For more information, please visit: https://www.morganstanley.com/people-opportunities/eeo.
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