Lead Data Analyst, Life Sciences AI & Data Platforms

Elsevier; City Of London; Vor Ort; GBP 90.000 - 120.000; Vollzeit

Benefits dieser Stelle

Health Insurance

Life Insurance

Flexible Work

Employee Assistance

Medical Screening

Parental Support

Paid Time Off

Transport

Zusammenfassung

Elsevier in London is seeking a Team Lead for Scientific Discovery Platforms to guide a team of 5–10 Data Analysts focused on quality, discoverability, and relevance of search and API experiences across Reaxys, Embase, and future AI-enabled life sciences solutions.

You will collaborate with Life Sciences, Data, Product and Technology teams to ensure data integrity and deliver data-driven improvements that researchers rely on.

Qualifikationen

• Academic background (Masters/PhD) in Bioinformatics, Cheminformatic/Chemistry Biochemistry or allied fields.
• Strong data analysis and scripting skills using Python (Jupyter Notebook) and Bash.
• Demonstrated AI/GenAI testing frameworks and analytical workflows experience.
• Experience with databases (e.g., PostgreSQL) and cloud platforms (AWS/Azure).

Aufgaben

• Lead SMEs evaluating Reaxys and Embase search and API quality.
• Improve discoverability through data models and pipelines.
• Oversee customer-focused QA, testing, and feature releases.
• Escalate unexpected search/API changes with expert input.
• Provide usage insights for platform improvements.
• Represent customer perspectives during product development.

Kenntnisse

Data analysis

Python (Jupyter)

Bash

Team collaboration

AI testing

Ausbildung

Masters/PhD in Bioinformatics/Cheminformatic/Chemistry Biochemistry

Tools

PostgreSQL

AWS

Azure

Jobbeschreibung

Elsevier in London is seeking a Team Lead for Scientific Discovery Platforms to guide a team of 5–10 Data Analysts focused on quality, discoverability, and relevance of search and API experiences across Reaxys, Embase, and future AI-enabled life sciences solutions.

You will collaborate with Life Sciences, Data, Product and Technology teams to ensure data integrity and deliver data-driven improvements that researchers rely on.