Machine Learning Engineer

Job Title:    Machine Learning Engineer

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. For over 175 years our brands and imprints have been a trusted source of knowledge to these communities and today, more than ever, we see it as our responsibility to ensure that fundamental knowledge can be found, verified, understood and used by our communities – enabling them to improve outcomes, make progress, and benefit the generations that follow.

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. The group meets the needs of Springer Nature’s Research division which includes Nature, Springer, BioMedCentral and Scientific American, as well as developing new data products for the research community via Nature Research Intelligence.  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

• Good networker, able to build up effective relations with internal business partners

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