Frontline Data Scientist β€” Personalization at Scale

Frontline Data Scientist β€” Personalization at Scale

Full-Time 60750 - 74250 Β£ / year (est.) Home office (partial)
United States Digital Space LLC

At a Glance

  • Tasks: Design and deploy ML solutions for personalised experiences with global brands.
  • Company: Join Braze, a forward-thinking company focused on equity and opportunity.
  • Benefits: Enjoy hybrid work options, competitive salary, and continuous learning opportunities.
  • Other info: Autonomy and growth in a dynamic team setting.
  • Why this job: Make a real impact by powering personalised experiences in a fast-growing environment.
  • Qualifications: Experience in data science and strong collaboration skills required.

The predicted salary is between 60750 - 74250 Β£ per year.

Braze is hiring for a Forward-Deployed Data Scientist to design and deploy end-to-end ML solutions powering personalized experiences for global brands.

You’ll own data-to-model deployment, work with clients, and guide cross-functional teams in a fast-growing environment.

The role emphasizes autonomy, collaboration, and continuous learning, with hybrid work options and a focus on equity and opportunity across the organization.

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Frontline Data Scientist β€” Personalization at Scale employer: United States Digital Space LLC

United States Digital Space LLC is an exceptional employer, offering a dynamic work culture that prioritises innovation and collaboration in the heart of Greater London. With a strong focus on employee well-being and flexible work options, we provide ample opportunities for professional growth and development, making it an ideal environment for those looking to make a meaningful impact in the field of AI-enabled SaaS engineering.

United States Digital Space LLC

Contact Details:

United States Digital Space LLC Recruitment Team

We think you need these skills to ace Frontline Data Scientist β€” Personalization at Scale

Machine Learning
Data Modelling
Client Engagement
Cross-Functional Collaboration
Autonomy
Continuous Learning
Personalisation Techniques