Business Analyst

Role Overview

We are seeking an experienced Senior Business Analyst to join a large-scale R&D data transformation programme within the pharmaceutical and life sciences sector.

This role sits at the intersection of laboratory science, scientific instrument data, and business/data analysis. The successful candidate will analyse and interpret raw data generated by laboratory instruments and equipment, translate scientific outputs into structured business requirements, and create the mappings, documentation, and business rules required to support downstream data and analytics initiatives.

The role is particularly suited to a senior Business Analyst with experience in Life Sciences/Pharma R&D, laboratory data, scientific instruments, data mapping, and data governance. You will act as a bridge between scientists, laboratory/informatics teams, data architects, data engineers, and business stakeholders.

Key Responsibilities Scientific & Instrument Data Analysis

• Analyse raw data generated by laboratory instruments and equipment, including plate readers, Raman spectrometers, cell counters, and similar analytical instruments.
• Understand instrument data structures, formats, attributes, metadata, and their underlying scientific/business meaning.
• Interpret complex scientific outputs and translate them into clear, structured business and data requirements.
• Identify relationships between instrument-generated data and downstream R&D processes and business outcomes.

Business Analysis & Requirements

• Conduct requirements gathering with scientists, laboratory operations teams, informatics specialists, data teams, and business stakeholders.
• Translate scientific and laboratory requirements into clear business requirements, functional specifications, and user stories.
• Define business rules, transformation logic, acceptance criteria, and data requirements.
• Manage and track requirements, user stories, and delivery activities using Azure DevOps (ADO).

Data Mapping & Documentation

• Create and maintain detailed data mapping specifications between raw instrument outputs and standardised business/data models.
• Map instrument data to relevant taxonomies, ontologies, data domains, and enterprise data models.
• Document data lineage from instrument/source systems through to downstream data lakes, analytics platforms, and other consuming systems.
• Develop and maintain data dictionaries, business glossaries, process flows, data flow diagrams, and mapping documentation.

Data Quality & Governance

• Identify data quality issues, inconsistencies, gaps, and ambiguities within scientific/instrument data.
• Work with data governance, architecture, engineering, and scientific teams to define appropriate resolution strategies.
• Ensure scientific context and business meaning are accurately represented within technical data models.
• Support data governance and standardisation initiatives across R&D data domains.

Stakeholder Collaboration

• Act as the key liaison between laboratory scientists, scientific SMEs, informatics teams, data architects, data engineers, IT, and data governance teams.
• Facilitate discussions between scientific and technical stakeholders to establish a common understanding of data requirements.
• Translate complex scientific concepts and instrument outputs into terminology and requirements that technical delivery teams can implement.
• Support alignment between scientific objectives, business requirements, and technical data solutions.

Testing & Validation

• Support User Acceptance Testing (UAT) and validation of data mappings, business rules, and transformation logic.
• Validate mappings and requirements against real-world instrument data and expected scientific outcomes.
• Investigate discrepancies and work with relevant stakeholders to resolve data or requirements issues.

Required Skills & Experience

• 8+ years of experience as a Business Analyst, preferably within Pharmaceutical, Life Sciences, Biotechnology, or Scientific R&D environments.
• Strong experience in data-focused Business Analysis, including data mapping, requirements gathering, and requirements documentation.
• Practical understanding of laboratory and scientific instrument data.
• Experience working with or exposure to instruments such as:
  • Plate readers
  • Raman spectrometers
  • Cell counters
  • Other analytical/laboratory instruments
• Ability to interpret raw scientific or instrument data and translate it into structured business logic, requirements, and data models.
• Experience working with data governance, data quality, data architecture, or data engineering teams within large enterprise environments.
• Strong experience producing data dictionaries, business glossaries, mapping specifications, process flows, and data lineage documentation.
• Hands-on experience with Azure DevOps (ADO) for Agile delivery, including backlog management, user stories, acceptance criteria, and sprint tracking.
• Strong stakeholder management and communication skills, with the ability to work effectively with both scientific SMEs and senior technical/business stakeholders.
• Excellent analytical, documentation, and problem-solving skills.