Jobs at NielsenIQ

Research and apply state-of-the-art machine learning techniques, primarily in NLP, to large-scale business problems. Conduct feasibility studies, analyze large datasets, develop scalable and maintainable production-grade ML solutions, and collaborate on deployment. Communicate findings to technical and non-technical stakeholders, assess real-world applicability, and contribute to team development and collaboration.

Design, develop, and deliver scalable front-end applications, services, APIs, and reusable UI components. Write clean, maintainable code; build proofs of concept; ensure software quality and maintainability; and collaborate within an agile team using Angular, TypeScript, JavaScript, testing tools, GitHub, JIRA, and Figma.

Build production-grade generative AI applications and scalable Python pipelines using LangChain, LangGraph, and MCP. Architect distributed systems and modular Angular microfrontend applications, deploy workloads on Azure, and implement observability with Coralogix and LangSmith. Collaborate with cross-functional teams to deliver enterprise AI solutions. Preferred experience includes LLM fine-tuning, prompt engineering, Docker, Kubernetes, MLOps, and automated deployment pipelines.

Design, develop, troubleshoot, and enhance ETL applications using Python, SQL, FastAPI, Snowflake, PostgreSQL, Airflow, and Azure. Ingest and transform data from multiple sources, build reportable data marts, create monitoring utilities, and deploy components through AI-enabled CI/CD pipelines. Collaborate with Agile teams and business stakeholders while applying data warehousing, object-oriented programming, cloud, DevOps, and microservices practices.

Leads advanced analytics projects, translating complex data into commercially useful recommendations for clients and internal teams. Develops statistical methodologies, reporting tools, and analytical processes; evaluates data sources; ensures quality and confidentiality; manages project scope and stakeholders; and mentors analysts through training and guidance.

Leads the technology strategy, roadmap, and delivery for Consumer Panel Services. Oversees scalable data platforms, cloud transformation, data processing, analytics, governance, security, compliance, and incident response. Partners with Product, Operations, Data Science, Enterprise IT, and Architecture teams while managing risks and timelines. Builds and mentors global engineering teams, drives Agile and DevSecOps adoption, and delivers secure, high-performance consumer data solutions.

Supports and improves an enterprise Product Development Lifecycle intake process for technology and operations initiatives. Responsibilities include reviewing and coordinating requests, managing governance workflows, monitoring queues and service levels, maintaining records, coordinating stakeholder meetings, and providing leadership updates. The role also develops dashboards and reports, supports Jira and Power Platform workflows, identifies automation opportunities, and drives process improvements across cross-functional business and technology teams.

Manage EU reference data operations, ensuring accurate, timely delivery through quality governance, coding validation, KPI monitoring, root-cause analysis, and stakeholder coordination. Identify opportunities for AI, automation, analytics, and process improvement; support implementation of intelligent controls and workflow solutions. Maintain procedures, resolve data issues, communicate risks and trends, coordinate cross-functional teams, and provide process guidance and training across Western European markets.

Develops scalable automated testing frameworks and test suites for cloud and big-data platforms. Responsibilities include Python/PySpark automation, SQL and service testing, manual and performance testing, CI/CD deployment, monitoring utilities, integration testing, and GenAI automation. The role collaborates with development, infrastructure, operations, and client teams while providing technical guidance and maintaining test documentation.

Business Analyst responsible for gathering and documenting requirements, translating business needs into user stories and acceptance criteria, analyzing processes and data, facilitating stakeholder workshops, and supporting software delivery, UAT, and product quality improvements. The role collaborates with Product, Engineering, QA, and global business teams in an agile environment while identifying automation and AI-driven optimization opportunities.

Lead the architecture, development, deployment, and operation of AI/ML solutions for identity and access security. Build scalable pipelines, inference services, monitoring, and MLOps capabilities using cloud platforms. Integrate anomaly detection and behavioral analytics into IAM, PAM, and secrets-management workflows. Partner with cybersecurity and engineering teams, establish applied AI security standards, influence technical roadmaps, and mentor junior engineers.

Lead global MLOps delivery by designing scalable software architectures, data pipelines, and machine learning solutions. Guide architecture decisions, collaborate with research, architecture, and product teams, and coordinate software delivery. Manage and mentor developers, conduct code reviews, maintain documentation, promote engineering best practices, oversee testing and CI/CD, and remain accountable for technical debt and system quality.

Leads global market research and business projects from initiation through completion. Responsibilities include project planning, scheduling, resource and risk management, stakeholder communication, governance, reporting, KPI tracking, and continuous improvement. The role uses Smartsheet and AI tools such as Microsoft Copilot, ChatGPT, and Gemini to improve planning, documentation, reporting, automation, and decision-making while coordinating cross-functional teams and ensuring timely, high-quality delivery.

Designs and optimizes SQL Server databases, ETL/ELT pipelines, data warehouses, dimensional models, and Power BI reporting solutions. Performs data validation, query and workload optimization, pipeline monitoring, troubleshooting, and deployment activities. Partners with stakeholders to define analytics requirements and delivers dashboards, KPIs, and scalable data solutions using Azure Data Factory, Azure Data Lake Storage, DAX, and Power Query.

Leads an engineering team delivering scalable full-stack and AI-enabled products. Responsibilities include setting architecture and coding standards, improving Java APIs and Angular applications, guiding AI solution development, enforcing DevSecOps and testing practices, managing technical roadmaps, mentoring engineers, overseeing delivery, optimizing cost and performance, and evaluating emerging technologies. The role is approximately 50% leadership and 50% hands-on engineering, including architecture, coding, design reviews, and technical problem-solving.

Research, evaluate, and apply state-of-the-art ML (primarily NLP) techniques to large-scale business problems. Perform feasibility studies, EDA, and develop production-quality, scalable, reproducible ML solutions. Communicate results to stakeholders, collaborate on deployment, maintain high code and data quality, and contribute to team building and cross-functional integration.