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About This Role
Job Title: GCS, Data Scientist
Who we are looking for
We 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.
Why This Role is important to us
Cybersecurity 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.
What you will be responsible for
As a Data Scientist, you will:
- Develop statistical, machine\-learning, and AI\-driven models that identify patterns, anomalies, relationships, and risk signals across enterprise cybersecurity datasets.
- Analyze large structured, semi\-structured, graph, time\-series, and text\-based security datasets using Python, SQL, PySpark, and Databricks.
- Design and deliver analytics that support threat detection, vulnerability prioritization, incident enrichment, cyber risk scoring, and security posture measurement.
- Apply graph analytics and network science techniques to uncover relationships among identities, assets, vulnerabilities, applications, alerts, events, and threat indicators.
- Use NLP and GenAI techniques to summarize, classify, enrich, and operationalize cybersecurity data such as alerts, tickets, logs, findings, playbooks, and investigation notes.
- Build reusable analytical datasets, features, notebooks, models, dashboards, and model\-monitoring outputs that can scale across enterprise security use cases.
- Partner with cybersecurity analysts, data engineers, platform engineers, architects, risk teams, and product owners to translate business and security needs into analytical solutions.
- Create Power BI reports and self\-service dashboards that communicate model outputs, cyber trends, operational performance, and risk insights to technical and non\-technical audiences.
- Support responsible model development practices, including validation, performance monitoring, explainability, privacy, security, lineage, and documentation in a regulated environment.
- Continuously evaluate emerging analytics, ML, graph, NLP, GenAI, SIEM, SOAR, and cloud data capabilities for practical application to cybersecurity outcomes.
Education \& Preferred Qualifications
Minimum Qualifications
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- 5\-8 years of total professional experience in data science, analytics, machine learning, data engineering, or related technical roles.
- 2\-4 years of experience applying data science, analytics, or machine learning techniques to cybersecurity, risk, fraud, infrastructure, identity, or similarly complex enterprise datasets.
- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Cybersecurity, Information Systems, or equivalent practical experience.
- Strong hands\-on experience with Python and SQL for analytical modeling, exploratory analysis, statistical evaluation, and data manipulation.
- Experience using PySpark and Databricks to process, analyze, and model large\-scale datasets.
- Experience creating clear, actionable dashboards and visualizations using Power BI or similar business intelligence tools.
- Working knowledge of AWS data and analytics services or cloud\-based analytical environments.
- Familiarity with SIEM/SOAR platforms and the security workflows they support, including alert enrichment, detection analytics, triage, response, and reporting.
- Experience with one or more advanced analytical methods such as graph analytics, NLP, GenAI, anomaly detection, classification, clustering, forecasting, or recommendation techniques.
- Strong written and verbal communication skills, including the ability to document analytical assumptions, model limitations, and recommended actions.
Preferred Qualifications
----------------------------
- Master's degree or advanced coursework in Data Science, Computer Science, Statistics, Applied Mathematics, Cybersecurity, or a related field.
- Experience operationalizing ML or AI solutions through feature pipelines, model monitoring, MLOps practices, reproducible notebooks, or production analytical workflows.
- Experience using graph frameworks, graph databases, knowledge graphs, entity resolution, embeddings, or relationship\-based analytics for security or risk use cases.
- Experience applying NLP or GenAI to summarize, classify, extract, or enrich cybersecurity information from unstructured or semi\-structured sources.
- Familiarity 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.
- Experience working in regulated, financial services, or enterprise\-scale technology environments where security, governance, privacy, and auditability are important.
- Relevant certifications or training such as Security\+, CySA\+, GIAC, CISSP, AWS, Databricks, or machine\-learning certifications are helpful but not required.
Technical Skills
====================
Skill Area
Expected Capabilities
Data Science / ML
Statistical modeling, supervised and unsupervised learning, feature engineering, model evaluation, anomaly detection, experimentation, explainability.
Programming \& Data
Python, SQL, PySpark, Databricks notebooks/jobs, scalable data preparation, analytical datasets, reusable feature pipelines.
Visualization \& BI
Power BI dashboards, operational metrics, executive\-ready reporting, trend analysis, self\-service analytical products.
Cloud \& Platforms
AWS analytical environments, cloud data services, secure data handling, scalable batch and interactive analytics.
Cybersecurity Tools
SIEM/SOAR workflows, alert enrichment, cyber telemetry, vulnerability data, identity risk, incident and response datasets.
Advanced Analytics
Graph analytics, NLP, GenAI, relationship analytics, entity resolution, text extraction, summarization, classification, and enrichment.
Additional Requirements
===========================
- This is an individual contributor role with strong cross\-functional collaboration expectations.
- The role will require sound judgment when working with sensitive cybersecurity, risk, operational, and regulated data.
- The candidate should be comfortable balancing exploratory data science, production\-minded analytical delivery, and stakeholder communication.
What We Value
These skills will help you succeed in this role:
- Strong analytical judgment, intellectual curiosity, and the ability to frame ambiguous cybersecurity problems as measurable data science opportunities.
- Hands\-on data science capability, including feature engineering, model development, statistical analysis, experimentation, and model performance evaluation.
- Practical understanding of cybersecurity concepts, including threat detection, vulnerabilities, identity and access risk, cyber incidents, SIEM/SOAR workflows, and security telemetry.
- Ability to communicate complex analytical findings clearly to cybersecurity operators, engineers, risk stakeholders, and senior leaders.
- Collaborative working style with a bias for reusable solutions, documentation, operational discipline, and measurable business impact.
Salary Range:
$90,000 \- $157,500 Annual
The 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.
*Employees 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.*
*For a full overview, visit* *https://hrportal.ehr.com/statestreet/Home* *.*
About State Street
======================
Across 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.
We 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.
As 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.
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Salary Context
This $90K-$157K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At State Street, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($123K) sits 36% below the category median. Disclosed range: $90K to $157K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
State Street AI Hiring
State Street has 13 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Quincy, MA, US, Boston, MA, US, Cambridge, MA, US. Compensation range: $157K - $282K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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