Machine Learning Engineer

Springer Nature is hiring for the role of Machine Learning Engineer!

Responsibilities of the Candidate:

• Building, deploying, and monitoring machine learning models and pipelines.
• Developing and implementing ML solutions with the broader team.
• Advocating for MLOps best practices, enhancing the adoption and standardization across the organization.
• Leading initiatives towards advanced analytics using statistical modeling, machine learning, and AI.
• Within 3 months you will:
• Familiarize yourself with the existing data and analytics technologies, including Google BigQuery, Vertex AI, neo4j and LangGraph.
• Build initial machine learning models and become part of the weekly sync-ups. 
• By 3-6 months you will:
• Collaborate in enhancing the machine learning infrastructure and supporting broader business integration.
• Deepen your understanding of the organization’s data sources, and contribute to models and product features for impactful decision-making. 
• By 6-12 months you will:
• Mentor new team members and contribute to recruitment efforts.
• Lead efforts to increase the use of predictive analytics and advocate for the adoption of advanced data techniques.
• Engage with various stakeholders to ensure alignment in ML solution implementation.

Requirements:

• You have a University degree with a strong analytical/quantitative background or equivalent experience (e.g. Data Science, Statistics, Mathematics, Econometrics, Physics, Computer Science etc.)
• You hold strong statistical and machine learning skills with a desire to continually learn and apply new knowledge
• You demonstrate experience in using machine learning to add tangible value in achieving the wider goals and strategy of the business
• You have demonstrable experience in working with various stakeholders, such as data scientists, engineers or product managers
• You are familiar with scientific publishing data
• You possess a strong working knowledge of SQL, Python, and Git, along with solid experience in at least one cloud environment, such as GCP (preferred), AWS or Azure
• You are adept at data extraction, cleansing, and interpretation using tools like SQL, Big Query and Python
• You have Demonstrable experience deploying models and pipelines in a cloud environment, such as GCP
• You are familiar with embedding models, vector search systems and large language models, including prompt engineering
• You have past experience with Plotly Dash
• You communicate effectively in English, both written and spoken, and enjoy networking to build relationships across the company
• You are well-organized, with strong problem-solving skills and business acumen