{"id":197417,"url":"https://alion.io/job/jpmorganchase-cybersecurity-aiml-lead-data-scientist","title":"Cybersecurity AI/ML Lead - Data Scientist","company":{"id":257,"name":"JPMorganChase","domain":"jpmorganchase.com","url":"https://alion.io/company/jpmorganchase","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":90,"open_postings":1129,"ghost_share":0.01,"stale_share":0.543,"repost_share":0.058,"time_to_fill_p50_days":12,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Wilmington, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":202000,"max_usd":418000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":782},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"CI/CD","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Interpretability","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Matplotlib","optional":false},{"name":"Pandas","optional":false},{"name":"Plotly","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Seaborn","optional":false},{"name":"SQL","optional":false},{"name":"AWS","optional":true},{"name":"Embeddings","optional":true},{"name":"TCP/IP","optional":true}],"status":"live","first_seen_at":"2026-09-03T00:00:00Z","employer_posted_date":"2026-09-03","last_verified_at":"2026-10-29T14:13:28Z","board_verified":true,"closed_at":null,"days_open":56,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":56},"description":"As a Cybersecurity AI/ML Lead - Data Scientist - Data Scientist at JPMorgan Chase within the Cybersecurity & Technology Controls, you will be an integral part of a team that develops advanced analytical and machine learning solutions to address complex cybersecurity and technology risk challenges. As a core technical contributor, you will help design and deliver scalable, auditable, and data-driven solutions that support our Cyber Operations teams.\nYou'll be conducting data analysis, statistical modeling, machine learning, and deep learning techniques to solve cybersecurity and technology risk problems. You will be able to prepare and analyze complex datasets, develop and evaluate models, and communicate findings clearly to technical and business stakeholders. You'll understand when Generative AI, transformer architectures, and related techniques are appropriate for applied security use cases.\nJob responsibilities\nPartner with stakeholders, business leaders, cybersecurity engineers, and data engineers to understand security needs, define use cases, and acquire the data required to address them.\nPerform exploratory data analysis on security and technology datasets, identify meaningful patterns, and communicate findings to stakeholders.\nSelect, develop, and evaluate statistical, machine learning, deep learning models that are appropriate for cybersecurity use cases and business outcomes.\nPrepare model-ready datasets through feature engineering, data quality assessment, and other data preparation techniques.\nLeads reuse-first adoption of enterprise-authorized AI capabilities within the work environment to accelerate data architecture and model analysis and strategic decisioning, with human-in-the-loop validation and appropriate handling of sensitive data.\nEstablishes portfolio-level guardrails for AI-assisted and agentic workflows used in data engineering design and delivery, including traceability/auditability and control expectations aligned to resiliency and security standards.\nSupport model governance by documenting model selection, interpretability, testability, performance, limitations, and results.\nDesign, build, review, debug, and maintain secure, high-quality production code for analytical and machine learning solutions.\nContribute to security control effectiveness by applying industry insights, internal standards, and regulatory expectations to improve security processes and protocols.\nAdd to a team culture of diversity, equity, inclusion, and respect.\nRequired qualifications, capabilities, and skills\nObtain 5 plus years of experience with formal training or certification in security engineering concepts \nWorking knowledge of probability, statistics, statistical distributions, and their application to cybersecurity or technology risk use cases.\nAdvanced Python skills, including Pandas, SQL, and data visualization tools such as Matplotlib, Seaborn, or Plotly.\nExperience leading teams in the safe use of enterprise-authorized AI capabilities within the work environment for security engineering workflows, including validation habits and awareness of data sensitivity.\nAbility to review and validate AI-assisted security recommendations before adoption, escalating uncertainty and ensuring outcomes align to security, resiliency, and auditability expectations.\nExperience using notebooks such as Jupyter, SageMaker, or VS Code to analyze data, document methods, and communicate results.\nWorking knowledge of Scikit-Learn for classification, regression, and clustering models, plus machine learning or deep learning frameworks such as PyTorch.\nExperience preparing complex datasets for modeling, including data cleaning, feature engineering, and data quality assessment.\nAbility to explain model selection, interpretability, performance metrics, and limitations verbally and in writing.\nProficiency with Software Development Life Cycle, CI/CD practices, application resiliency, and secure software delivery.\nIn-depth knowledge of the financial services industry and related IT systems.\nPreferred qualifications, capabilities, and skills\nBachelor’s degree in Data Science, Mathematics, Statistics, Econometrics, Computer Science, or a related field, plus 3+ years of applied data science experience.\nExperience with TCP/IP networking, cybersecurity technologies, and security-related telemetry.\nExperience monitoring models in production and identifying data quality, drift, or performance issues.\nExperience deploying statistical or machine learning models in production environments, including AWS SageMaker.\nWorking knowledge of Large Language Models, natural language models, vector embeddings, and responsible AI practices such as fairness, reliability, and 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