Senior Clinical Data Analyst

MResult; Bengaluru; On-site; INR 1,300,000 - 2,100,000; Full time

Job summary

MResult is seeking a data analytics specialist with Biopharma/Life Sciences experience to translate clinical data requirements into actionable analytics. You will work with data engineers, BI developers, and clinicians to deliver reliable datasets, KPIs, and reporting logic across CTMS, EDC, and RIM systems.

Strong SQL, data profiling, and governance skills are essential to identify data quality issues, drive remediation, and ensure accurate analytics for decision making.

Qualifications

• Bachelor's or Master's in Life Sciences, Pharmacy, Biotechnology, Clinical Research, Statistics, Data Science, CS, Engineering or related field.
• 5-8 years of experience in Clinical Data Analysis, Clinical/R&D Analytics or related roles.
• Proven experience with Biopharma/Life Sciences clients.
• Strong understanding of Clinical/R&D processes, systems, and data.
• Experience with CTMS, EDC, RIM/Regulatory systems, and clinical planning systems.

Responsibilities

• Gather and translate business requirements into data requirements, metrics, and specifications.
• Analyse datasets to identify trends, anomalies, and data quality issues.
• Understand data across source systems and downstream analytical use.
• Perform data profiling, reconciliation, validation, and root-cause analysis using SQL.
• Define and validate KPIs, data definitions, and reporting logic.
• Collaborate with Data Engineers, BI Developers, and stakeholders to deliver analytics solutions.
• Contribute to data products, data lineage initiatives, and semantic models.
• Support UAT, test cases, defect investigation, and sign-off.
• Facilitate workshops to clarify requirements and resolve data issues.
• Maintain documentation across delivery lifecycle and governance activities.

Skills

Biopharma Client

Life Sciences

Clinical/R&D

CTMS & EDC

RIM & TMF

SQL & Data

Data Quality

Stakeholder Mgmt

Agile/Scrum

Education

Bachelor's or Master's in Life Sciences

Tools

Planisware

Jira

Azure DevOps

Job description

Responsibilities:

• Gather, analyse, and translate Clinical/R and D business requirements into data requirements, business rules, metrics, and analytical specifications.
• Analyse clinical and R and D datasets to identify trends, anomalies, inconsistencies, and data- quality issues.
• Understand data across key Clinical/R and D source systems, their relationships, data flows, and downstream analytical use.
• Perform data profiling, reconciliation, validation, source-to-target analysis, and root-cause analysis using SQL and other analytical tools.
• Define and validate clinical/R and D KPIs, metrics, data definitions, and reporting logic.
• Collaborate with Data Engineers, Data Architects, BI Developers, and Clinical/R and D stakeholders to deliver analytics solutions.
• Contribute to analytical datasets, semantic models, data products, and data lineage initiatives.
• Support UAT, test case development, defect investigation, and business sign-off.
• Facilitate workshops and stakeholder discussions to clarify requirements and resolve data-related issues.
• Maintain functional, data, and analytical documentation throughout the delivery lifecycle.
• Support data governance activities including data quality, Critical Data Elements (CDEs), metadata, and lineage.
• Support Agile delivery activities including backlog refinement, user stories, acceptance criteria, and sprint execution.

Requirements:

• Bachelor's or Master's degree in Life Sciences, Pharmacy, Biotechnology, Clinical Research, Statistics, Data Science, Computer Science, Engineering, Business or a related field.
• 5-8 years of experience in Clinical Data Analysis, Clinical/R and D Analytics, Data Analysis, Business Analysis, or related roles.
• Proven experience working with Biopharma / Pharmaceutical / Life Sciences clients.
• Strong understanding of Clinical/R and D processes, systems, and data.
• Experience working with or analysing data from systems such as CTMS, EDC, RIM/Regulatory systems, clinical planning systems, and other R and D platforms.
• Understanding of clinical product hierarchies, study/trial structures, site and subject data, milestones, and related R and D data domains.
• Strong SQL and data analysis capabilities.
• Experience with data quality, validation, reconciliation, data mapping, and root-cause analysis.
• Understanding of data warehousing, BI, reporting, data integration, and analytical data models.
• Strong communication, stakeholder management, documentation, and problem-solving skills.
• Experience working with cross-functional and geographically distributed teams.
• Familiarity with Agile/Scrum and tools such as Jira or Azure DevOps.

Must-Have Skills:

• Biopharma/Life Sciences Client Experience - proven experience working with Biopharma, Pharmaceutical, Biotechnology, Clinical Research, or Life Sciences clients.
• Clinical/R and D Domain Knowledge - strong understanding of clinical development, clinical operations, clinical trials, and R and D processes.
• Clinical/R and D Systems and Data - practical understanding of systems such as CTMS, EDC, RIM, eTMF, Study Startup, and clinical planning/portfolio management systems such as Planisware, along with the data generated and consumed across these systems.
• Clinical Data Structures - understanding of clinical product/asset hierarchies, study/trial structures, sites, subjects, milestones, and relationships between key R and D data domains.
• SQL and Data Analysis - ability to independently query, analyse, validate, reconcile, and troubleshoot complex datasets.
• Data Quality and Validation - experience with data profiling, reconciliation, business-rule validation, issue investigation, and root-cause analysis.
• Data Mapping and Lineage - understanding of source-to-target mapping, data transformations, data definitions, and data lineage across systems.
• Requirements and Analytical Thinking - ability to translate Clinical/R and D business needs into data requirements, business rules, metrics, and analytical specifications.
• Stakeholder Management - ability to work effectively with Clinical/R and D SMEs, business stakeholders, data engineers, architects, and BI teams.