Fidelity

Job Description:

Position Description:

Develops supervised and unsupervised Machine Learning (ML) models -- regression, decision trees/random forest, clustering, neural networks, SVM, K-Means, and time series. Improves surveillance and screening capabilities, using ML principles and emerging technologies. Designs, develops, and optimizes Anti-money laundering (AML) detection and sanctions models. Partners with colleagues to build software and data solutions that monitor transactions and sanctions systems across multiple business units, using Python and SQL. Researches, develops, and delivers models to detect suspicious activity within typologies that include, but are not limited to, cryptocurrency trading, cryptocurrency receipt/delivery, market manipulation (equities, options, and cryptocurrencies), securities fraud, insider trading, elder financial exploitation, and money-laundering and terrorist financing domains.

Primary Responsibilities:

• Oversees the planning, design, and execution of multiple ML-based projects.
• Enables learning, collaboration, and mentoring of software engineers and data scientists.
• Performs ML lifecycle management and de-bugging for bias, drift, and fragility.
• Adheres to engineering and data science best-practices (Git, documentation, and test automation).
• Sources, cleanses, and joins data from diverse sources.
• Implements emerging technologies into screening programs.
• Enables innovation and the decommission of legacy systems

Education and Experience:

Bachelor’s degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and five (5) years of experience as a Senior Manager, Data Science (or closely related occupation) building algorithms to deploy applications in a financial services environment, using programming languages and Machine and Deep Learning frameworks.

Or, alternatively, Master’s degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and two (2) years of experience as a Senior Manager, Data Science (or closely related occupation) building algorithms to deploy applications in a financial services environment, using programming languages and Machine and Deep Learning frameworks.

Or, alternatively, PhD degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and no experience.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) sampling data with complex SQL queries from Snowflake and MongoDB databases, performing data analysis, preprocessing, feature engineering, and visualization, using Python and SQL languages and libraries -- Pandas, NumPy, SciPy, Matplotlib, Seaborn, and Tableau.
• DE performing advanced statistical analytics, feature selection and reduction, parameter tuning, training supervised and unsupervised models on imbalanced datasets, and model evaluation, using Python ML libraries (IMBLearn, scikit-learn, and LightGBM) and techniques (regression, decisions trees, random forest, LightGBM, XGBoost, neural networks, and clustering) for the purpose of anomaly detection; and training text classification models and named entity recognition models using Spacy library.
• DE communicating and collaborating across teams to break down complex business problems, translating into ML projects, leading project development, and delivering products and insights for productization, using collaboration tools (JIRA and Confluence).
• DE performing development, maintenance, version controlling, migration, and change assessment with test runs and back testing on ML model pipelines, using platforms and tools (Amazon Web Services (AWS), S3 storage services and EC2 Cloud computing services, Python, SQL, DBT, Snowflake, and GIT.

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Certifications:

Category:

Data Analytics and Insights

Fidelity’s hybrid working model blends the best of both onsite and offsite work experiences. Working onsite is important for our business strategy and our culture. We also value the benefits that working offsite offers associates. Most hybrid roles require associates to work onsite every other week (all business days, M-F) in a Fidelity office.

Please be advised that Fidelity’s business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.