Your mission

As part of the Data Science team, the Scientific Data Engineer will divide their time between digitalization and data analysis. The candidate will develop and maintain the R&D department’s data model, data pipelines and dashboarding to support R&D activities. These span a wide range of areas including electronic lab notebook templatization and integrations, data capture and storage, results processing, dashboarding, and statistical analysis activities. They will also share responsibility with the wider Data Science team for providing technical support to scientists across these areas.

Job Description

Major Activities

• Write well-documented and well-tested ELN templates that follow company best practices.
• Develop tools to maintain and ensure data integrity throughout the data lifecycle.
• Develop SQL queries to improve data accessibility for operational and scientific user-groups.
• Implement dashboards to improve data accessibility across the R&D organisation.
• Perform data mining, data exploration and feature generation to bring proof of concept AI/ML / statistical / mechanistic / hybrid modelling in collaboration with data scientists and bench scientists.
• Serve as part of the technical support team for software utilised by R&D staff.
• Train end-users on adequate use of tools released for general use.
• Produce rigorous documentation of all software development processes.
• Maintain up-to-date knowledge on state-of-the-art data analysis practices applicable to life sciences R&D.

Key Performance Indicators

• Accurately and effectively translate user requirements into successful ELN templates and data pipelines.
• Timely prioritisation and resolution of user- reported bugs / requested enhancements.
• Working on multiple unrelated projects concurrently while adhering to agreed timelines.

Key Job Competencies

• Problem Solving - Identifies and resolves problems in a timely manner; gathers and analyzes information skilfully; develops alternative solutions; works well in group problem solving situations; uses reason even when dealing with emotional topics.
• Motivation - sets and achieves challenging goals; demonstrates persistence and overcomes obstacles; measures self against standard of excellence.
• Planning/Organizing - prioritizes and plans work activities; uses time efficiently; plans for additional resources; sets goals and objectives; organizes or schedules other people and their tasks; develops realistic action plans.
• Professionalism - works well under pressure; treats others with respect and consideration regardless of their status or position; accepts responsibility for own actions; follows through on commitments.
• Innovation - displays original thinking and creativity; meets challenges with resourcefulness; generates suggestions for improving work; develops innovative approaches and ideas.
• Oral Communication - speak clearly and persuasively in positive or negative situations; listens and gets clarification; responds well to questions; demonstrates good presentation skills; participates effectively in meetings.
• Written Communication - writes clearly and informatively; edits work for spelling and grammar; varies writing style to meet needs; presents numerical data effectively.

 

Job Responsibilities

No direct reports.
 

Job Background

Essential

• BS/MS degree in Data Science/Data Analysis/Bioprocess Engineering or related discipline
• Understanding of bioprocessing and/or experience working with bioprocess development data
• Advanced knowledge of Python and SQL.

Desired

• Previous industrial experience in pharma ELN maintenance (can include internships/placements).
• Software / Database / Bioprocess model development experience.
• Knowledge of dashboarding tools such as Tableau, Spotfire and PowerBI.

Why us?