Machine Learning Engineer

Machine Learning Engineer page is loaded

Location: London (Hybrid, min. 2 days per week in the office)

About Springer Nature Group

Springer Nature opens the doors to discovery for researchers, educators, clinicians and other professionals. Every day, around the globe, our imprints, books, journals, platforms and technology solutions reach millions of people.

About the Role

Springer Nature is seeking a Machine Learning Engineer for its highly-regarded Research Intelligence team, part of the Data and Analytics Solutions group. This is an exciting opportunity as Data Science and Machine Learning are expanding from strong foundations into new solutions, and we are looking for someone who is able to deliver solutions and work independently, with support from the wider team where necessary.

Role Responsibilities:

• Build machine learning models, tools, and pipelines that support Springer Nature and its customers to advance discovery.
• Implement cutting edge ML and data science solutions in partnership with the wider team.
• Increase the adoption of ML and MLOps, standardise ML practices across the organization, improve model management and reporting, and make data led insight accessible to the business.
• Being an advocate for MLOps, share thought leadership on tools and best practices.
• Leading the journey towards more advanced and predictive analytics using statistical modelling, machine learning and AI.

Experience, Skills & Qualifications:

Essential

• University degree with a strong analytical/quantitative background or equivalent experience (e.g. Data Science, Statistics, Mathematics, Econometrics, Physics, Computer Science etc.).
• Strong working knowledge of SQL, Python, and Git.
• Strong knowledge of at least one cloud environment, such as GCP (preferred), AWS or Azure.
• Ability to extract, cleanse and combine data and make sense of it using e.g. SQL, Big Query, Python.
• Strong statistical and machine learning skills and the desire to learn more.
• Excellent communication skills.
• Demonstrable experience deploying models and pipelines in a cloud environment, such as GCP.
• Demonstrable experience working with embedding models and vector search systems.
• Demonstrable experience of using machine learning to add tangible value in achieving the wider goals and strategy of the business.
• Excellent analytical problem solving capabilities coupled with business acumen.
• Well organized and accurate with good time management.

Desirable

• Demonstrable experience with Plotly Dash.
• Demonstrable experience with large language models, including prompt engineering.
• Demonstrable experience working with various stakeholders, such as data scientists, engineers or product managers.
• Demonstrable experience working with scientific publishing data.

Springer Nature is a Disability Confident Committed Employer and we encourage applications from candidates with disabilities. If you consider yourself to have a disability or learning difficulty and wish to submit your application in an alternative format or would like to discuss reasonable adjustments during the application and interview process, please get in touch.

At Springer Nature we value the diversity of our teams. We strive for an inclusive workplace that empowers all our colleagues to thrive.