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Senior Data Scientist II
RELXSenior Data Scientist building generative-AI, retrieval, and agentic systems for LexisNexis Legal & Professional. Delivering reliable Python applications powering legal information and analytics.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates advanced Python proficiency in designing, implementing, and refactoring production applications while ensuring modularity, observability, and performance. Capable of owning the full lifecycle of AI components, including testing, validation, and incident response.
Highest-signal resume keywords
Advanced Python ProficiencyAutomated Testing with PytestProduction ObservabilityModular Application DesignIncident Response and Remediation
ATS Keywords
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Hard Skills
Python ProgrammingData StructuresAlgorithmsObject-Oriented DesignFunctional DesignType AnnotationsComplexity AnalysisCode ReviewPerformance OptimizationProduction Readiness
Soft Skills
Problem-SolvingCollaborationAttention to Detail
Tools & Technologies
PytestRuffMypyPydanticCI Quality Gates
Industry Keywords
AI ComponentsLLM ConcernsObservabilityIncident InvestigationRoot-Cause Analysis
Tech Stack
Tools & technologiesPython
About the role
Key responsibilities & impact- Architect modular agentic applications separating retrieval, prompt construction, model invocation, tool execution, state and history management, orchestration, validation, and response formatting
- Refactor complex or legacy Python code to improve correctness, readability, modularity, extensibility, testability, and runtime performance
- Own production readiness for AI components, including validation, exception handling, timeouts, retries, fallbacks, configuration, and secure credential handling
- Establish observability for LLM and retrieval workflows through logging, metrics, tracing, alerting, and error reporting
- Design interfaces and data contracts across retrieval, orchestration, model, and downstream application components
- Write unit, integration, regression, and end-to-end tests, including failure-mode and dependency-availability tests
- Review Python and agentic application code and identify architectural and operational risks
- Diagnose and optimize latency, memory usage, retrieval performance, token consumption, model cost, and scalability
- Apply data structures, algorithms, and computational-complexity analysis to solutions
- Participate in deployments, incident investigation, root-cause analysis, remediation, and reliability improvements
Requirements
What you’ll need- Advanced Python proficiency designing, implementing, debugging, testing, reviewing, and refactoring production applications
- Strong command of Python fundamentals, data structures, algorithms, object-oriented and functional design, type annotations, and complexity analysis
- Ability to transform prototype or experimental code into modular, maintainable, observable, production-ready systems
- Strong understanding of separation of concerns, dependency injection, interface design, configuration management, and abstraction
- Experience with automated unit, integration, regression, and end-to-end testing using tools such as pytest
- Experience designing resilient distributed applications handling timeouts, retries, rate limits, partial failures, malformed responses, idempotency, and graceful degradation
- Experience with production observability, including structured logging, metrics, tracing, alerting, and incident troubleshooting
- Ability to conduct rigorous code reviews and identify correctness, maintainability, performance, security, testing, and operational risks
- Experience owning applications across design, experimentation, deployment, monitoring, incident response, and ongoing improvement
- Strong understanding of production LLM concerns, including structured output validation, context management, model and tool failures, prompt versioning, token and cost controls, security, and evaluation
- Preferred: Python quality tooling including pytest, ruff, mypy, profiling tools, and CI quality gates
- Preferred: typed schemas and LLM input/output validation using tools such as Pydantic
- Preferred: evaluation frameworks for agentic systems
- Preferred: model fallbacks, tool-use controls, guardrails, human-in-the-loop workflows, and AI auditability
- Preferred: production service support, incident response, root-cause analysis, and post-incident remediation
Benefits
Comp & perks- Flexible hours
- Wellbeing initiatives
- Shared parental leave
- Study assistance
- Sabbaticals
- Country-specific benefits
- Accommodation or adjustment support for applicants with disabilities or other needs