Senior Data Scientist - Digital Twins

Kantar Group; Reading; On-site; GBP 55,000 - 75,000; Full time

Benefits offered by this job

Flexible hybrid working

25 days leave

Competitive salary and bonus

Company pension

Enhanced parental leave

Healthcare options

Wide range of flexible benefits

Job summary

Kantar Group is looking for a Senior Data Scientist based in Reading. You will apply advanced modeling and analytics to enhance the performance of high-volume data systems.

The role involves developing machine-learning models, working across the data science lifecycle, and collaborating with engineers to ensure production success. Benefits include flexible hybrid working, competitive salary with a bonus, and various healthcare options.

Qualifications

• Solid experience working as a data scientist on real-world, production problems.
• Strong Python skills (or a similar language used in applied data science).
• Experience with supervised learning and unsupervised techniques such as anomaly detection.

Responsibilities

• Developing and deploying machine-learning models in production environments.
• Working end-to-end across the data science lifecycle.
• Analysing new and existing data sources.

Skills

Data Science experience

Python skills

Supervised learning

Unsupervised techniques

Cloud-based training and deployment

Tools

AWS

Azure

Job description

Location: Reading, King’s Road
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This role is about applying data science where it genuinely matters — in live, production environments that support large‑scale research operations around the world.

As a Senior Data Scientist on the Digital Twins team you’ll work on advanced modelling and analytics that improve the quality, reliability and performance of high‑volume data systems. You’ll partner closely with engineering, operational and commercial teams, taking ideas from hypothesis through to deployment, and ensuring models deliver real value once they’re live.

You’ll join a collaborative team of data scientists, analysts and engineers, working on complex, mission‑critical platforms that underpin the insights delivered to some of the world’s best‑known organisations.

Digital Twins at Kantar is an AI platform that simulates consumer behaviour at scale, enabling Kantar’s products to test scenarios and predict outcomes before they happen.

What you’ll be doing

• Developing and deploying machine‑learning models in production environments
• Working end‑to‑end across the data science lifecycle: problem definition, modelling, deployment and monitoring
• Analysing new and existing data sources to improve decision‑making and model performance
• Designing feedback loops that continuously improve outcomes and data quality
• Collaborating closely with engineers to deliver scalable, reliable model predictions
• Communicating technical insights clearly to non‑technical stakeholders

The skills & experience you’ll need

• Solid experience working as a data scientist on real‑world, production problems
• Strong Python skills (or a similar language used in applied data science)
• Experience with supervised learning and unsupervised techniques such as anomaly detection
• Exposure to cloud‑based model training, deployment and automation
• A strong sense of ownership and accountability for data science outputs

Experience working with large consumer datasets or high‑traffic platforms is an advantage, but not essential.

Our tech environment

You’ll work with modern data and analytics tooling across cloud platforms, including machine‑learning services, data pipelines and monitoring solutions. Our current ecosystem includes AWS technologies, with new development increasingly aligned to Azure.

What’s in it for you

• Flexible hybrid working
• 25 days leave, 2 days paid for volunteering and life event leave
• Competitive salary and bonus (bonus dependent on role)
• Company pension
• Enhanced parental leave
• Healthcare options
• Wide range of flexible benefits

We are not able to offer visa sponsorship or assist with relocation support for this role. Please ensure you have the right to work in the country where this role is located before applying.