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

 Robust 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.

📌 Lab Informatics-Data Analyst (Mumbai)
🏢 Trigent Software
📍 Mumbai