Lab Informatics-Data Analyst

Role Overview The Data Science & Analytics Lead is responsible for driving data-driven decision making
across Research, Development, Quality Control, and Manufacturing laboratories.
The role combines expertise in laboratory informatics, scientific data management,
advanced analytics, AI/ML, and business intelligence to transform laboratory data into
actionable insights.
The individual will partner with scientists, laboratory operations teams, IT, data
engineers, and business stakeholders to establish analytics capabilities leveraging ELN,
LIMS, SDMS, CDS, and enterprise data platforms.
This role serves as the technical lead for Analytics initiatives, ensuring seamless data
flow, automation, regulatory compliance, and interoperability across R&D, Quality
Control and Manufacturing environments.
The role requires strong consulting, stakeholder management, and problem-solving
skills, along with a solid understanding of laboratory operations and digital technologies. Job Profile
(Detailed
description of
Role,
Responsibilities
and Functions) Analytics & Data Science
Define and execute Laboratory analytics and data science strategy.
Develop advanced analytics solutions to improve laboratory performance and
scientific outcomes.
Apply statistical analysis, predictive modelling, and machine learning
techniques.
Create models for:
Experimental optimization
Process monitoring
Predictive quality
Equipment utilization
Sample lifecycle analytics
Research productivity metrics
Identify opportunities for AI-driven automation and insight generation.
Analyse data from systems like ELN, LIMS, SDMS, CDS, MES, QMS platform
and build unified laboratory data models.
Support integration of laboratory and enterprise data assets.
Training and implementation of various Analytics model.
Business Intelligence & Reporting
Build executive dashboards and KPI frameworks.
Develop visualizations using tools like Power BI, Spotfire and Tableau.
Deliver insights for R&D Productivity, Laboratory Operations & Quality
Metrics.
Define analytics metrics and performance indicators. AI & Machine Learning
Lead development and deployment of:
Predictive analytics
Machine learning models
Generative AI use cases
NLP solutions for scientific documentation
Collaborate with data engineering teams to operationalize AI solutions.
Evaluate emerging AI technologies relevant to laboratory environments. Required Skills
& Experience Manager: 8+ years of experience in Data Science, Analytics, or Laboratory Informatics.
5+ years working in Life Sciences, Pharma, Biotechnology, or Chemicals industry.
Experience with scientific data platforms and laboratory systems.
Experience leading analytics initiatives across complex enterprise environments. Required Skills
and Experience Strong understanding of
Analytics & Data Science
Python
R
SQL
Machine Learning
Statistical Modeling
Predictive Analytics
Data Mining
NLP
laboratory operations across:
Research & Development (R&D)
Analytical Development
Quality Control (QC)
Quality Assurance (QA)
Manufacturing Laboratories
Stability Management
Understanding of Scripting and automation tools
Knowledge of laboratory workflows, sample lifecycle, instrument integration, and
laboratory compliance
Lab Informatics Knowledge: Hands-on experience with one or more of the following:
LIMS, ELN, SDMS, CDS, LES
Exposure to leading platforms: Revvity Signals, Dotmatics, Benchling, STARLIMS,
LabWare, IDBS, Waters Empower, Agilent OpenLab, BIOVIA Preferred
Qualifications Bachelor's or Master's degree in Biotechnology, Biomedical Engineering, Chemical
Engineering, Pharmacy, Life Sciences, Computer Science, Information Technology, or
related discipline.
Azure Data Science Certification/Databricks Certification is an added advantage. Experience in regulated environments (GxP, GLP, GMP, 21 CFR Part 11, ALCOA+, Data
Integrity).
Familiarity with laboratory standards, validation, and quality management processes.
Exposure to GenAI platforms such as Azure OpenAI, Copilot Studio, and AI Agents is
desirable.