About The Role

A Data Analyst II should have a basic understanding of best practices and can execute on projects and initiatives with supervision from others. Individuals will create basic level insights and recommendations in their area of expertise. Individuals in this role will continue to provide support to the analytics team members and begin to lead analytics efforts with low complexity.

Responsibilities

This role is located within DataOps and supports data scientists working within the Domains of the Research Data Platform. Domains are functional units that are responsible for delivering one or more data products, often through data science algorithms, and supporting this work could lead to a wide range of different analytical activities.

For example, you may be asked to dive into large datasets to answer questions from product owners or data scientists; you may need to perform large-scale data preparation (dataprep) in order to test hypotheses or support prototypes; you may be asked to review the precision and recall of data science algorithms at scale and surface these as dashboard metrics.

You will need to have a keen eye for detail, good analytical skills, and expertise in at least one data analysis system. Above all, you will need curiosity, dedication to high quality work, and an interest in the world of scientific research and the products that Elsevier creates to serve it.

Because you will need to communicate with a range of stakeholders around the world we ask for candidates to demonstrate a high level of English.

Requirements

Minimum work experience of 3 years

Coding skills in at least one programming language (preferably Python) and SQL

Familiarity with common string manipulation functions such as regular expressions (regex)

Prior exposure to data analysis in a tabular form, for example with Pandas or Apache Spark/Databricks

Knowledge of basic statistics relevant to data science – eg. precision, recall, F-score

Knowledge of visualization tools such as Tableau/Power BI is a plus

Experience of working with Agile tools such as JIRA is a plus

Stake Holder Management

Build and maintain strong relationships with Data Scientists and Product Managers.

Align activities with Data Scientists and Product Managers.

Present achievements and project status updates, both written and verbally, to various stakeholders.