Data Scientist II

Elsevier•Philadelphia, PA

2d•$71,600 - $119,400

About The Position

What if your next AI model could help accelerate a medical breakthrough, uncover a critical scientific insight, or help researchers solve some of humanity's greatest challenges? At Elsevier, data science is about far more than algorithms and model performance. It is about applying advanced AI to help researchers, clinicians, educators, and institutions discover knowledge, assess evidence, generate insights, and advance science for the benefit of society. Every day, millions of researchers rely on our products to navigate an ever-growing universe of scientific information. As a Data Scientist II, you will help build the intelligent systems that make scientific knowledge more discoverable, trustworthy, connected, and actionable. This is AI with purpose. This is technology in service of scientific progress. About the Role In this role, you will design and build machine learning, NLP, and generative AI solutions that support clinical knowledge discovery, evidence extraction, decision support, and intelligent content understanding. You will work with large-scale clinical and scientific content and data, applying the right techniques to solve complex problems and deliver reliable, production-ready systems. Working closely with cross-functional partners, you will help turn ambiguous clinical and scientific challenges into measurable outcomes that improve how clinicians and researchers discover and apply knowledge. About the team As part of a growing team of Data Scientists, you will take on some of the hardest problems in science. This team is building intelligent systems that can reason across scientific publications, research data, knowledge graphs, ontologies, metadata, taxonomies, citations, and content spanning every scientific discipline

Requirements

Experience in data science, machine learning, artificial intelligence, NLP, statistics, applied mathematics, computer science, or a related quantitative area.
Experience working with frontier LLMs such as OpenAI's GPTs, Anthropic's Claude, and Google's Gemini, including fine-tuning LLMs and/or SLMs.
Strong Python skills and a habit of writing clean, maintainable, well-tested code.
A solid grasp of machine learning fundamentals, including supervised and unsupervised learning, feature engineering, model evaluation, model selection, and performance measurement.
Experience working with structured, semi-structured, or unstructured data, especially large-scale text or clinical/scientific content datasets.
Familiarity with common data science and machine learning tools such as Pandas, NumPy, SciPy, Scikit-learn, PyTorch, TensorFlow, or Matplotlib.
The ability to translate complex and ambiguous requirements into practical, measurable, data-driven solutions, with strong analytical thinking, problem-solving skills, and attention to quality.
Clear communication skills, a collaborative approach to working with engineering, product, clinical, and business stakeholders, and a genuine interest in building production-ready systems that improve health outcomes.

Responsibilities

Design and build machine learning, NLP, and generative AI systems for clinical knowledge discovery, evidence extraction, decision support, and intelligent content understanding.
Work with large-scale, complex, and heterogeneous data, including clinical documentation, scientific publications, research datasets, knowledge graphs, ontologies, taxonomies, citations, metadata, and content spanning every clinical and scientific discipline.
Apply the right technique to each problem, using approaches such as classification, regression, clustering, ranking, feature engineering, deep learning, embeddings, LLMs, retrieval, and generative AI.
Develop capabilities for semantic search, information retrieval, entity extraction, content classification, recommendation, ranking, summarization, question answering, and evidence-grounded generation for clinical and scientific use cases.
Build, evaluate, fine-tune, prompt, and integrate models into robust production systems, while continuously improving quality, relevance, reliability, and clinical/user value.
Write clean, tested, production-quality Python and contribute reusable data science components, packages, and scalable data pipelines for preprocessing, inference, experimentation, monitoring, and continuous improvement.
Support deployment, monitoring, model maintenance, drift detection, automated retraining, and ongoing optimization of data science systems that clinicians and researchers depend on.
Collaborate with engineering, product, UX, analytics, research, clinical, and domain experts, and communicate technical concepts, model behavior, insights, trade-offs, and recommendations clearly to technical and non-technical audiences.