Jobs at dunnhumby

Positions 221

dunnhumby is a global customer data science company that originated as Tesco's analytics arm. They specialize in using data, machine learning, and behavioral science to help retailers and brands create personalized experiences. Their tech stack includes Python, Spark, TensorFlow, and cloud platforms like AWS and GCP.

Careers at dunnhumby range from data science and engineering roles to product management, analytics consulting, and software development. Data scientists craft predictive models, data engineers build and maintain pipelines, ML engineers deploy models at scale, and product managers translate insights into customer‑facing features. Teams collaborate across regions and run frequent hackathons to prototype solutions.

By browsing dunnhumby listings on Job Transparency, you gain immediate access to published salary ranges, employee sentiment scores, and benefit breakdowns. This data lets you benchmark offers, identify pay gaps, and prepare stronger negotiation points before you even speak to a recruiter.

No open positions at dunnhumby right now.

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Frequently Asked Questions

What is it like to work at dunnhumby?
<p>dunnhumby fosters a data‑centric culture where every team member is encouraged to experiment and share findings. Employees enjoy flexible remote options, quarterly hackathons, and a mentorship program that pairs junior analysts with senior data scientists. The company values continuous learning, offering paid certifications in cloud, ML, and data engineering, and promotes a transparent feedback loop through regular 360 reviews.</p>
What types of positions are available at dunnhumby?
<p>Key roles include Data Scientist, Data Engineer, Machine Learning Engineer, Analytics Consultant, Product Manager, Software Engineer, Business Analyst, and Data Visualization Specialist. Each role focuses on building scalable data pipelines, developing AI models, or translating insights into market‑ready products.</p>
How can I stand out as an applicant for dunnhumby roles?
<p>Stand out by showcasing a portfolio of projects that demonstrate end‑to‑end data solutions, such as a recommendation engine or a churn prediction model. Include code samples on GitHub, explain the business impact, and highlight any open‑source contributions. Also prepare to discuss how you collaborate with cross‑functional teams and how you keep up with new ML frameworks.</p>

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