Data Scientist - Vice President

Preferred candidates will have a strong working knowledge of common workflows for data analysis, data preparation, and model development. They must have a working knowledge of data analysis and manipulation tools, statistics (e.g. statistical distributions and probability), and experience applying supervised and unsupervised learning models to solve well‑defined problems. They should possess the ability to develop statistical and deep learning models, measure their outcomes, and interpret them for business stakeholders. Candidates should have a working knowledge of generative AI models, transformer architectures, and when to apply these tools and techniques. As a Lead Data Scientist at JPMorgan Chase within the Cybersecurity and Technology Controls line of business, you are an integral part of a team that delivers machine‑learning solutions that satisfy pre‑defined functional and user requirements, with an added dimension of detection and prevention of misuse, circumvention, and malicious behavior. As a core technical contributor, you are responsible for carrying out critical technology solutions with tamper‑proof, audit‑defensible methods across multiple technical areas within various business functions. Job Responsibilities Works with stakeholders and business leaders to understand security needs and recommend business modifications during periods of vulnerability. Works with cybersecurity engineers and data engineers to acquire data that addresses each use case (fraud, anomaly detection, cyber threats). Performs exploratory data analysis on datasets and communicates results to stakeholders. Selects statistical or deep learning models that are best positioned to achieve business results. Performs feature engineering or hyperparameter tuning to optimize model performance. Performs model governance activities for model interpretability, testability and results. Executes creative security solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions and break down technical problems. Develops secure and high‑quality production code and reviews and debugs code written by others. Minimizes security vulnerabilities by following industry insights and governmental regulations to continuously evolve security protocols, including creating processes to determine the effectiveness of current controls. Required Qualifications, Capabilities, and Skills Formal training or certification on security engineering concepts and 5+ years of applied experience. Advanced proficiency in one or more programming languages. Advanced understanding of agile methodologies such as CI/CD, application resiliency, and security. Working knowledge of probability, statistics and statistical distributions and their applicability to use cases, plus the ability to perform exploratory data analysis using Jupyter or SageMaker notebooks. Proficient in Pandas, SQL and data‑visualization tools such as Matplotlib, Seaborn or Plotly. Working knowledge of Scikit‑Learn for development of classification, regression and clustering models and deep learning frameworks such as PyTorch. Experience with feature engineering complex datasets. Ability to explain model selection, model interpretability and performance metrics verbally and in writing. Preferred Qualifications, Capabilities, and Skills Bachelor's degree in Data Science, Mathematics, Statistics, Econometrics or Computer Science and 7+ years of data‑science experience (exploratory data analysis, statistical analysis and reporting results). Experience with knowledge graphs, graph analytics and graph databases (a plus). Working knowledge of large language models (LLM), NLP, embedding models and vector databases. Experience with retrieval‑augmented generation (RAG) applications and the frameworks used to create them such as LangChain or LlamaIndex. Experience with AI agent frameworks such as Google ADK and Langraph. Experience deploying statistical or machine‑learning models via AWS SageMaker in a production setting. Knowledge of responsible AI, model fairness, reliability and safety. Benefits We offer a competitive total rewards package, including a base salary determined based on the role, experience, skill set and location. Eligible roles may receive commission‑based pay and/or discretionary incentive compensation, paid in the form of cash or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs, based on eligibility, including comprehensive health‑care coverage, on‑site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental‑health support, financial coaching and more. EEO Statement We recognize that our people are our strength and that the diverse talents they bring to our global workforce contribute to our success. We are an equal‑opportunity employer and place a high value on diversity and inclusion. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental‑health or physical disability needs. #J-18808-Ljbffr Koitecc Solutions