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State Street

Holds assets for institutions and runs index funds

Data Scientist & Automation Lead - Vulnerabilities remediation, Governance

Applied Machine LearningUpdated on Nov 11
Full-timeLondon, UKNo salary listedIn-person

About the Job

Requirements

  • Strong experience in data science, machine learning, and advanced analytics to develop predictive, operational, and insight-driven solutions.
  • Proficiency in Python, SQL, and data engineering for data transformation, modelling, automation, and analytics development.
  • Strong proficiency in visualization tools such as Power BI and Tableau to design interactive dashboards, self-service reporting tools, and stakeholder-ready business intelligence solutions.
  • Strong problem definition and design thinking skills to convert ambiguous business needs into user-centered, scalable analytics and automation solutions.
  • Ability to work in ambiguous environments where business problems may not be clearly defined at the start, shaping direction, asking questions, and creating clarity with stakeholders.
  • Experience leveraging SharePoint, Microsoft 365, and enterprise collaboration technologies to deliver digital solutions and knowledge management capabilities.
  • Proven ability to design and implement API integrations and connected systems across multiple platforms and data sources.
  • Experience in process mining, workflow optimization, and intelligent automation to improve operational efficiency.
  • Strong background in solution architecture, technical design, and scalable application development.
  • Demonstrated success delivering analytics products and business intelligence solutions from concept through deployment and adoption.
  • Knowledge of cloud, AI, generative AI, and Copilot technologies, with the ability to translate emerging technologies into practical business solutions.
  • Expert capability in applied machine learning, optimization, and operational research techniques.
  • Strong Python expertise with experience delivering production-quality data science software.
  • Experience working with cloud platforms and modern machine learning tooling.
  • Strong software engineering discipline including testing, continuous integration/continuous deployment, and version control.
  • Ability to structure complex business and technical problems and propose pragmatic solutions.
  • Bachelor's, Master's, or Doctorate degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or a related field, and data-science experience such as managing structured and unstructured data, applying statistical techniques, and reporting results.
  • At least five years of experience working on production machine learning or optimization software products at scale.
  • Demonstrated experience or a strong interest in customer-facing environments.
  • Experience developing industrialized software, especially data science or machine learning software products.
  • Strong communication across technical and non-technical audiences.
  • Collaborative working style focused on continuous improvement.
  • Systems-thinking mindset with attention to detail.
  • Curiosity, resilience, and adaptability in fast-moving environments.
  • Commitment to delivering measurable business value.

Responsibilities

  • Acquire data necessary for vulnerability remediation and plan and develop usable datasets for modelling.
  • Update internal best practices for data collection and preparation, and contribute to data integrity discussions with stakeholders, management, and threat and vulnerability management teams.
  • Evaluate the team's models, recommend improvements, drive modelling best practices, and develop operational models that run at scale.
  • Review data analysis and modelling techniques, and identify and invent new evaluation methods.
  • Research and maintain knowledge of industry trends and technologies to identify strategic opportunities and contribute to thought leadership best practices.
  • Write extensible code across multiple features and develop expertise in debugging techniques.
  • Define business, customer, and solution strategy goals, and partner with other teams to identify and explore opportunities.
  • Apply a customer-oriented focus to understand customer needs and help set realistic customer expectations.
  • Act as the technical owner of one or more production-grade data science product codebases.
  • Lead end-to-end delivery of decision-support software across the full product lifecycle.
  • Understand complex operational business problems end to end and identify opportunities for data-driven optimization.
  • Drive modelling and system design decisions, balancing implementation time, system behavior, technical debt, and business value.
  • Prototype, build, and deliver industrialized machine learning and optimization models in Python.
  • Design and build robust data ingestion, cleaning, and processing pipelines.
  • Implement orchestration pipelines and continuous integration/continuous deployment processes.
  • Ensure software quality through logging, error handling, and automated testing.
  • Harden algorithms against operational and data edge cases.
  • Quantify product adoption and value capture.
  • Engage with business stakeholders to gather requirements and feedback.
  • Contribute to feature prioritization and roadmap discussions.
  • Support integration of decision-support products into operational business processes.
  • Communicate modelling approaches, trade-offs, and results to technical and business audiences.
  • Mentor and support junior team members.
  • Contribute to agile ways of working, including Git versioning, code reviews, and delivery predictability.
  • Act as a trusted technical advisor to global compliance leaders and business stakeholders.
  • Lead solution discussions, proof-of-concepts, demonstrations, and technology evaluations.
  • Translate business requirements into technical solutions and drive execution from concept through deployment.
  • Mentor team members and promote adoption of modern analytics and AI practices.
  • Lead design and implementation of Microsoft Copilot, AI agents, and generative AI solutions supporting vulnerability remediation oversight and governance operations.
  • Develop and deploy Copilot agents, policy assistants, chatbot solutions, and knowledge retrieval capabilities to improve user experience and productivity.
  • Evaluate emerging AI technologies and translate business opportunities into scalable enterprise solutions.
  • Partner with governance, compliance, and technology teams to deliver AI-powered automation, advanced analytics, digital monitoring, and Copilot-enabled solutions that enhance compliance operations globally.
  • Translate complex challenges into scalable, data-driven solutions that improve risk management, decision-making, operational efficiency, and user experience across multiple regions.

Desired Qualifications

  • Working knowledge of Power Platform and Power Automate, particularly for workflow automation and rapid prototyping.
  • Experience in the finance and banking sector.

About the Company

State Street

Holds assets for institutions and runs index funds

State Street holds and services investments for pension funds, insurers, fund companies, and other large institutions, and it also runs index funds through State Street Investment Management. It launched the first US-listed exchange-traded fund, the SPDR S&P 500 ETF Trust, in 1993, and in 1924 it became custodian of the first US mutual fund. As a custody bank, it safekeeps clients' stocks and bonds, completes their trades, calculates what their funds are worth, and lends their securities to other investors. It earns fees on the assets it services and manages, plus income from currency trading and from interest on client cash. State Street traces its roots to a bank chartered in 1792 and is based in Boston, Massachusetts.

Company size
10,001+
Company stage
IPO
Headquarters
Boston, MA
Founded
1792

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