Principal Data Scientist I

Reed Technology•Raleigh, NC

1d•$118,300 - $219,800

About The Position

We are looking for a Principal Data Scientist I to join our Data Science / Embedded AI Innovation team at LexisNexis. This role is ideal for a highly skilled and experienced generalist data scientist who can independently lead complex AI, machine learning, NLP, analytics, experimentation, evaluation, cloud, and application development initiatives. In this role, you will provide technical and execution leadership across applied AI solutions, generative AI use cases, machine learning models, data science experimentation, application development, AWS-based solution design, and production evaluation frameworks. You will play a key role in shaping technical direction, improving AI quality, defining measurable outcomes, and driving best practices for responsible, scalable, and reliable AI delivery. You will also help manage the team’s technical work by coordinating priorities, breaking down work, identifying dependencies, tracking execution, and helping remove blockers. This is a senior individual contributor role and does not include direct people-management responsibility. The ideal candidate is not limited to traditional data science work. They should be able to move from problem discovery to modeling, experimentation, application development, cloud deployment, evaluation, and production support. They should be comfortable building AI-enabled applications and services that can be used by internal teams, product teams, and customers.

Requirements

Strong generalist data science background across machine learning, NLP, analytics, experimentation, applied AI, application development, and cloud-based solution delivery.
Hands-on experience designing, building, evaluating, and improving machine learning, AI-based, or data-driven applications.
Ability to build applications, prototypes, APIs, backend services, internal tools, automation workflows, or production-oriented AI capabilities.
Strong AWS skills, including hands-on experience with services such as S3, Lambda, IAM, API Gateway, CloudWatch, Step Functions, Glue, SageMaker, Bedrock, ECS, ECR, or similar AWS services.
Experience designing cloud-native solutions with attention to security, scalability, reliability, observability, cost, and operational readiness.
Experience with generative AI, LLM-based applications, prompt evaluation, retrieval-augmented generation, NLP, or related applied AI techniques.
Strong proficiency in Python and common data science / machine learning frameworks.
Experience with application development frameworks, APIs, backend services, or web application patterns.
Strong proficiency in SQL and experience working with large, complex datasets.
Strong understanding of statistical analysis, experimentation, model validation, evaluation design, and performance measurement.
Experience defining AI/ML quality metrics and translating model performance into business or product outcomes.
Experience working with engineering teams to move AI/ML solutions into production environments.
Ability to lead technical workstreams, coordinate execution, manage dependencies, and drive delivery without being a direct people manager.
Ability to operate independently in ambiguous problem spaces and create structure for broader team execution.
Strong communication skills with the ability to explain complex technical topics to senior stakeholders, product teams, and engineering teams.
Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Information Systems, Software Engineering, or a related technical field required.

Nice To Haves

Experience in customer support/operations related domains.
Experience building full-stack or backend applications that integrate AI/ML capabilities.
Experience with React, Streamlit, Flask, FastAPI, Node.js, or similar application development frameworks.
Experience with AWS Bedrock, including Agentcore, Redshift, OpenSearch, Lambda, ECS, EKS, or serverless architectures.
Experience with search, summarization, classification, extraction, entity recognition, ranking, recommendation systems, or document intelligence.
Experience building evaluation frameworks for generative AI, NLP, or retrieval-augmented generation systems.
Experience with cloud-based ML platforms, MLOps, CI/CD, model monitoring, observability, or production AI systems.
Experience with Databricks, AWS, data pipelines, feature engineering, or large-scale data processing.
Experience mentoring data scientists or engineers in a senior individual contributor capacity.
Experience influencing product and technical strategy across cross-functional teams.
Experience helping teams define delivery plans, manage technical priorities, and improve execution discipline.
Master’s degree or PhD in Data Science, Computer Science, Machine Learning, Statistics, Applied Mathematics, Engineering, or a related technical field preferred.

Responsibilities

Lead design, development, experimentation, and evaluation of AI/ML solutions across multiple product and engineering initiatives.
Operate as a generalist across machine learning, NLP, generative AI, analytics, experimentation, model evaluation, application development, cloud engineering, and applied data science use cases.
Develop and improve models, prompts, retrieval strategies, evaluation methods, and data-driven approaches for product-facing AI capabilities.
Apply statistical, machine learning, and experimentation techniques to solve complex customer and business problems.
Translate ambiguous business problems into structured data science approaches, measurable objectives, and executable delivery plans.
Design, build, and iterate on AI-enabled applications, prototypes, internal tools, APIs, services, and proof-of-concepts.
Develop working solutions that demonstrate business value and can evolve into production-ready capabilities.
Build application components that integrate models, data pipelines, prompts, retrieval systems, evaluation workflows, and user-facing experiences.
Partner with engineering teams to transition prototypes and data science solutions into scalable production systems.
Contribute to backend services, APIs, automation scripts, evaluation dashboards, and workflow tools needed to support AI delivery.
Ensure applications are designed for reliability, maintainability, observability, security, and scalability.
Design and develop cloud-native AI/ML and data science solutions using AWS services.
Use AWS capabilities such as S3, Lambda, API Gateway, IAM, CloudWatch, Redshift, Bedrock, Glue, ECS, ECR, and related services where appropriate.
Build scalable cloud-based workflows for AI experimentation, evaluation, data processing, model integration, and application deployment.
Partner with platform and engineering teams to ensure AWS-based solutions follow security, compliance, cost, and operational standards.
Improve performance, cost efficiency, reliability, and maintainability of cloud-based AI and data science applications.
Support production deployment patterns, monitoring, alerting, logging, and operational readiness for AI-enabled services.
Play a lead role in managing the team’s technical work, including work breakdown, prioritization support, sequencing, dependency tracking, and delivery coordination.
Lead complex data science and AI application workstreams from problem definition through research, experimentation, application development, validation, production readiness, and post-launch improvement.
Guide technical decisions related to model selection, data strategy, application design, AWS architecture, evaluation design, metrics, quality thresholds, and implementation approach.
Identify risks, blockers, and trade-offs early and communicate them clearly to engineering, product, and leadership stakeholders.
Help establish standards, reusable patterns, and best practices for AI/ML delivery, application development, and cloud-based implementation across the team.
Design and evaluate LLM-based and NLP-based solutions for document-heavy, research-oriented, and workflow-integrated use cases.
Build and improve evaluation frameworks for AI quality, including accuracy, relevance, completeness, groundedness, consistency, latency, and user impact.
Conduct error analysis, benchmarking, model comparisons, prompt testing, retrieval evaluation, and iterative quality improvement.
Partner with engineering teams to ensure AI solutions are observable, testable, reliable, and production-ready.
Support responsible AI practices, including explainability, governance, privacy, security, and compliance expectations.
Define success metrics and measurement strategies for AI/ML capabilities.
Design experiments and analyses to evaluate model performance, product impact, and user outcomes.
Use data to identify opportunities, validate assumptions, and guide product and engineering decisions.
Create clear narratives from complex data science findings and communicate recommendations to technical and non-technical stakeholders.
Support ongoing monitoring and improvement of deployed AI/ML capabilities.
Partner with Product Management, Engineering, Architecture, UX, Analytics, Platform, Security, and business stakeholders to deliver high-impact AI and data science solutions.
Translate customer, user, and business needs into scalable data science solutions, AI-enabled applications, and measurable delivery plans.
Work closely with engineering teams to productionize models, applications, data pipelines, evaluation workflows, and AI-enabled product features.
Mentor data scientists and engineers, provide technical guidance, and contribute to knowledge sharing across the team.
Influence technical direction across multiple initiatives without direct people-management responsibility.