{"id":1282333,"url":"https://alion.io/job/state-street-gcs-data-scientist-avp-2","title":"GCS, Data Scientist, AVP","company":{"id":1806,"name":"State Street","domain":"statestreet.com","url":"https://alion.io/company/state-street","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":86,"open_postings":261,"ghost_share":0.038,"stale_share":0.379,"repost_share":0.123,"time_to_fill_p50_days":31,"computed_at":"2026-11-10T06:52:35Z"}},"role":"Data Science","role_family":"Data Science","seniority":"head","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Quincy, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":90000,"max":157500,"currency":"USD","period":"year","gross":null,"usd_annual":157500},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Databricks","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SIEM","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false},{"name":"Anomaly Detection","optional":true},{"name":"AWS","optional":true},{"name":"Embeddings","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Spark","optional":true}],"status":"closed","first_seen_at":"2026-09-02T00:00:00Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-08T21:05:56Z","board_verified":false,"closed_at":"2026-11-08T21:05:56Z","days_open":67,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":67},"description":"Job Title: GCS, Data Scientist\nWho we are looking for\nWe are looking for a Data Scientist to support enterprise cybersecurity data science and analytics. This role will apply statistical modeling, machine learning, graph analytics, NLP, and GenAI techniques to large-scale security datasets to generate actionable insights, improve risk prioritization, enrich security operations, and help cybersecurity teams make faster, better-informed decisions. The ideal candidate combines strong data science depth with practical cybersecurity awareness and the ability to collaborate with security, engineering, governance, and risk stakeholders.\nWhy This Role is important to us\nCybersecurity teams increasingly rely on high-quality data, analytical models, and AI-enabled insights to prioritize risk, detect emerging issues, and respond effectively. This Data Scientist role strengthens the organization's ability to transform cybersecurity telemetry and operational data into predictive, explainable, and actionable intelligence. The role will help improve decision-making across security operations, risk management, vulnerability prioritization, threat detection, and enterprise cybersecurity reporting.\nWhat you will be responsible for\nAs a Data Scientist, you will:\nDevelop statistical, machine-learning, and AI-driven models that identify patterns, anomalies, relationships, and risk signals across enterprise cybersecurity datasets.\nAnalyze large structured, semi-structured, graph, time-series, and text-based security datasets using Python, SQL, PySpark, and Databricks.\nDesign and deliver analytics that support threat detection, vulnerability prioritization, incident enrichment, cyber risk scoring, and security posture measurement.\nApply graph analytics and network science techniques to uncover relationships among identities, assets, vulnerabilities, applications, alerts, events, and threat indicators.\nUse NLP and GenAI techniques to summarize, classify, enrich, and operationalize cybersecurity data such as alerts, tickets, logs, findings, playbooks, and investigation notes.\nBuild reusable analytical datasets, features, notebooks, models, dashboards, and model-monitoring outputs that can scale across enterprise security use cases.\nPartner with cybersecurity analysts, data engineers, platform engineers, architects, risk teams, and product owners to translate business and security needs into analytical solutions.\nCreate Power BI reports and self-service dashboards that communicate model outputs, cyber trends, operational performance, and risk insights to technical and non-technical audiences.\nSupport responsible model development practices, including validation, performance monitoring, explainability, privacy, security, lineage, and documentation in a regulated environment.\nContinuously evaluate emerging analytics, ML, graph, NLP, GenAI, SIEM, SOAR, and cloud data capabilities for practical application to cybersecurity outcomes.\nEducation & Preferred Qualifications\nMinimum Qualifications\n5-8 years of total professional experience in data science, analytics, machine learning, data engineering, or related technical roles.\n2-4 years of experience applying data science, analytics, or machine learning techniques to cybersecurity, risk, fraud, infrastructure, identity, or similarly complex enterprise datasets.\nBachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Cybersecurity, Information Systems, or equivalent practical experience.\nStrong hands-on experience with Python and SQL for analytical modeling, exploratory analysis, statistical evaluation, and data manipulation.\nExperience using PySpark and Databricks to process, analyze, and model large-scale datasets.\nExperience creating clear, actionable dashboards and visualizations using Power BI or similar business intelligence tools.\nWorking knowledge of AWS data and analytics services or cloud-based analytical environments.\nFamiliarity with SIEM/SOAR platforms and the security workflows they support, including alert enrichment, detection analytics, triage, response, and reporting.\nExperience with one or more advanced analytical methods such as graph analytics, NLP, GenAI, anomaly detection, classification, clustering, forecasting, or recommendation techniques.\nStrong written and verbal communication skills, including the ability to document analytical assumptions, model limitations, and recommended actions.\nPreferred Qualifications\nMaster's degree or advanced coursework in Data Science, Computer Science, Statistics, Applied Mathematics, Cybersecurity, or a related field.\nExperience operationalizing ML or AI solutions through feature pipelines, model monitoring, MLOps practices, reproducible notebooks, or production analytical workflows.\nExperience using graph frameworks, graph databases, knowledge graphs, entity resolution, embeddings, or relationship-based analytics for security or risk use cases.\nExperience applying NLP or GenAI to summarize, classify, extract, or enrich cybersecurity information from unstructured or semi-structured sources.\nFamiliarity with cybersecurity data sources such as endpoint telemetry, authentication logs, cloud security events, vulnerability findings, asset inventories, application records, network events, incident tickets, or threat intelligence.\nExperience working in regulated, financial services, or enterprise-scale technology environments where security, governance, privacy, and auditability are important.\nRelevant certifications or training such as Security+, CySA+, GIAC, CISSP, AWS, Databricks, or machine-learning certifications are helpful but not required.\nTechnical Skills\nSkill Area\nExpected Capabilities\nData Science / ML\nStatistical modeling, supervised and unsupervised learning, feature engineering, model evaluation, anomaly detection, experimentation, explainability.\nProgramming & Data\nPython, SQL, PySpark, Databricks notebooks/jobs, scalable data preparation, analytical datasets, reusable feature pipelines.\nVisualization & BI\nPower BI dashboards, operational metrics, executive-ready reporting, trend analysis, self-service analytical products.\nCloud & Platforms\nAWS analytical environments, cloud data services, secure data handling, scalable batch and interactive analytics.\nCybersecurity Tools\nSIEM/SOAR workflows, alert enrichment, cyber telemetry, vulnerability data, identity risk, incident and response datasets.\nAdvanced Analytics\nGraph analytics, NLP, GenAI, relationship analytics, entity resolution, text extraction, summarization, classification, and enrichment.\nAdditional Requirements\nThis is an individual contributor role with strong cross-functional collaboration expectations.\nThe role will require sound judgment when working with sensitive cybersecurity, risk, operational, and regulated data.\nThe candidate should be comfortable balancing exploratory data science, production-minded analytical delivery, and stakeholder communication.\nWhat We Value\nThese skills will help you succeed in this role:\nStrong analytical judgment, intellectual curiosity, and the ability to frame ambiguous cybersecurity problems as measurable data science opportunities.\nHands-on data science capability, including feature engineering, model development, statistical analysis, experimentation, and model performance evaluation.\nPractical understanding of cybersecurity concepts, including threat detection, vulnerabilities, identity and access risk, cyber incidents, SIEM/SOAR workflows, and security telemetry.\nAbility to communicate complex analytical findings clearly to cybersecurity operators, engineers, risk stakeholders, and senior leaders.\nCollaborative working style with a bias for reusable solutions, documentation, operational discipline, and measurable business impact.\nSalary Range:\n$90,000 - $157,500 AnnualThe range quoted above applies to the role in the primary location specified. If the candidate would ultimately work outside of the primary location above, the applicable range could differ.\nEmployees are eligible to participate in State Street’s comprehensive benefits program, which includes: our retirement savings plan (401K) with company match; insurance coverage including basic life, medical, dental, vision, long-term disability, and other optional additional coverages; paid-time off including vacation, sick leave, short term disability, and family care responsibilities; access to our Employee Assistance Program; incentive compensation including eligibility for annual performance-based awards (excluding certain sales roles subject to sales incentive plans); and, eligibility for certain tax advantaged savings plans.\nFor a full overview, visit https://hrportal.ehr.com/statestreet/Home.\nAbout State Street\nAcross the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.\nWe are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.\nAs an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.\nDiscover more information on jobs at StateStreet.com/careers\nRead our CEO Statement\nJob Application Disclosure:\nIt is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. 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