QA Engineer – Scientific Software / Life Sciences / IoT Systems

ob Description - QA Engineer – Scientific Software / Life Sciences / IoT Systems
Location Pune/ Hybrid, as per project requirement | Employment Type - Full-time | Experience 3–6 years
Link - https://talenthire360.com/jobs/6a9944f186c2707a8baba08b
Role Overview
We are looking for a detail-oriented QA Engineer with experience or strong interest in scientific software, life sciences, laboratory systems, healthcare, device-connected platforms, or regulated software
environments.
The selected candidate will be responsible for testing complex software applications involving laboratory workflows, instrument-connected systems, data processing, calculations, reporting, audit trails, and validation-oriented quality processes.
This role requires strong functional QA skills, analytical thinking, documentation discipline, and the ability to work closely with developers, business analysts, technical leads, domain experts, and client stakeholders.
Key Responsibilities
Functional Testing: Perform manual, regression, integration, system, and end-to-end testing of scientific and instrument-connected software applications.
Workflow Validation: Understand and validate workflows such as method setup, sequence creation, data processing, result review, reporting, and audit trail.
Test Case Preparation: Prepare detailed test scenarios, test cases, test data, and execution reports based on requirements and acceptance criteria.
Data & Result Validation: Validate calculated outputs, result tables, units, flags, pass/fail status, rounding rules, and summary values.
Report Testing: Verify generated reports against expected/reference outputs and ensure data correctness, traceability, and consistency.
Audit Trail Testing: Validate audit logging for key user actions, changes, recalculations, approvals, report generation, and data updates.
Defect Management: Log clear defects with reproducible steps, screenshots/logs, expected vs actual behavior, severity, and business impact.
UAT Support: Support UAT planning, execution, defect triage, evidence collection, and acceptance sign-off.
Regression Testing: Ensure new features do not impact existing workflows, calculations, reports, or data integrity.
Automation Support: Identify repeatable test scenarios and support automation teams in building regression suites.
Documentation: Maintain test plans, test cases, execution summaries, defect reports, traceability matrices, and QA evidence packs.
Preferred Exposure
Life Sciences / Pharma / Biotech: Useful for understanding regulated workflows and validation discipline.
Laboratory Software: Exposure to LIMS, ELN, CDS, scientific data systems, or lab workflow software is preferred.
Analytical Instruments: Experience with instrument-connected applications or scientific equipment software is an advantage.
IoT / Device Software: Useful for testing device status, connectivity, telemetry, logs, and data acquisition workflows.
Healthcare / MedTech: Helpful for quality, traceability, and compliance-oriented testing.
Industrial Automation: Useful for understanding device-driven workflows, alerts, and operational reliability.
Key Concepts Candidate Should Be Comfortable Learning
Sample: Material being tested or analyzed.
Method: Configuration used to run or process an analysis.
Sequence: Ordered list of samples or runs.
Calibration: Use of known standards to calculate unknown values.
Chromatography: Analytical workflow that produces peaks over time.
ICP-OES / MS / PDA: Scientific instrument or detector technologies that generate analytical data.
SST / QC: Quality checks used to confirm system or method performance.
Audit Trail: Record of who did what, when, and why.
Report Parity: Comparing generated output against a trusted reference report.
Traceability: Ability to link results back to source data, method, user, version, and actions.
Required Skills
Manual QA: Strong hands-on experience in functional, regression, integration, and end-to-end testing.
Test Design: Ability to convert requirements, workflows, and acceptance criteria into structured test cases.
API Testing: Working knowledge of REST APIs, Postman, JSON, request/response validation.
Database Testing: Ability to execute basic SQL queries and validate backend data.
Defect Reporting: Strong ability to document defects clearly and work with development teams for resolution.
Documentation: Experience preparing test plans, test cases, test reports, and QA evidence artifacts.
Analytical Thinking: Ability to identify edge cases, data mismatches, calculation issues, and workflow gaps.
Communication: Good written and verbal communication skills for working with technical and domain teams.
Good-to-Have Skills
Test Automation: Selenium, Playwright, Cypress, API automation, or equivalent tools.
Python Scripting: Useful for data comparison, file validation, report comparison, and automation utilities.
Report Comparison: Experience comparing PDF, Excel, CSV, or generated reports against expected outputs.
Scientific Data Testing: Ability to validate numeric values, units, rounding, calculations, and result consistency.
Compliance Exposure: Familiarity with audit trail, electronic records, validation documentation, or 21 CFR Part 11 concepts.
CI/CD Exposure: Understanding test execution in pipelines and regression dashboards.
Qualification Criteria
Education: B.E./B.Tech/MCA/M.Sc./B.Sc. in Computer Science, Life Sciences, Biotechnology, Instrumentation, Electronics, or related field.
Experience: 3–6 years of QA experience in software testing.
Domain Preference: Life sciences, healthcare, laboratory software, IoT, analytical instruments, or regulated software experience preferred.
Technical Skills: Manual testing, API testing, SQL basics, defect management, documentation.
Mindset: Detail-oriented, analytical, process-driven, quality-focused, and willing to learn scientific workflows.
Collaboration: Work effectively with developers, BAs, technical leads, and domain SMEs.
Quality Ownership: Think beyond UI testing and validate end-to-end workflow correctness.
Domain Adaptability: Quickly learn scientific and instrument-connected software concepts.

Work arrangement
No