Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Science Manager

NielsenIQ
Oxford
21 hours ago
Create job alert
Overview

You will be responsible for leading and managing a dynamic team of data scientists. You will play a crucial role in overseeing the team's sprints, workload reviews, OKR and goal setting, and regular career planning. Working with peers within the CGA Technology group (GBS), you will align the Data Science initiatives with the company’s strategic direction and ensure delivery of our strategic goals.

Responsibilities
  • Lead, mentor, and manage a team of data scientists to ensure a high level of performance and productivity.
  • Foster a collaborative and innovative team culture that encourages continuous learning and professional development.
  • Coordinate and facilitate work planning sessions to define project scope, goals, and deliverables.
  • Manage the execution of sprints, ensuring that the team meets deadlines and delivers high-quality results.
  • Conduct regular reviews of team members' workloads using JIRA, providing guidance on prioritization and resource allocation.
  • Collaborate with team members to identify potential challenges and implement solutions to optimize workload distribution.
  • Work closely with the team to establish clear and measurable departmental goals aligned with the organizational objectives.
  • Monitor progress towards goals, identify obstacles, and implement strategies to ensure targets are consistently met.
  • Establish effective communication channels with cross-functional teams to understand project requirements and ensure alignment with organizational goals.
  • Foster a collaborative working environment by facilitating effective communication and knowledge sharing between data science and other teams within the GBS function.
  • Identify opportunities for process improvement within the data science team, optimizing workflows, and implementing best practices.
  • Stay abreast of industry trends and emerging technologies to enhance the team's capabilities.
Qualifications
  • 5+ years' experience of leading Data Science teams
  • Experience with cloud computing and storage (MS Azure preferred).
  • Experience with DataBricks (Development and Deployment)
  • Strong knowledge of optimization and / or machine learning algorithms
  • Strong knowledge of Python and the respective development tools (e.g. Jupyter, PyCharm)
  • Experience in SDLC and version control platforms
Benefits
  • Flexible working environment
  • Volunteer time off
  • LinkedIn Learning
  • Employee-Assistance-Program (EAP)
About NIQ

NIQ is the world’s leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach. With a holistic retail read and the most comprehensive consumer insights—delivered with advanced analytics through state-of-the-art platforms—NIQ delivers the Full View™. NIQ is an Advent International portfolio company with operations in 100+ markets, covering more than 90% of the world’s population.

For more information, visit NIQ.com

Want to keep up with our latest updates?

Follow us on: LinkedIn | Instagram | Twitter | Facebook

Our commitment to Diversity, Equity, and Inclusion

At NIQ, we are steadfast in our commitment to fostering an inclusive workplace that mirrors the rich diversity of the communities and markets we serve. We believe that embracing a wide range of perspectives drives innovation and excellence. All employment decisions at NIQ are made without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other characteristic protected by applicable laws. We invite individuals who share our dedication to inclusivity and equity to join us in making a meaningful impact. To learn more about our ongoing efforts in diversity and inclusion, please visit https://nielseniq.com/global/en/news-center/diversity-inclusion


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Science Manager (Applied AI)

Data Scientist Manager

Data Scientist Project Lead

Data Science Engineering Manager - Audit

Data Science Engineering Manager - Audit (Hybrid)

Data Science Engineering Manager - Audit

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.