Job Title: Lifesciences Domain SME
Location: India (Bangalore/Pune/Hyderabad/Gurgaon)
Job Type: Full time
Job Summary:
We are seeking a highly skilled professional with expertise in computational biology, machine learning, and life sciences data analytics . The role focuses on genetic target identification, omics data analysis, AI/ML modeling (including QSAR & Geneformer), and real-world evidence (RWE) using modern cloud-based data platforms.
This role will partner with clients to shape solution strategies, evangelize data-driven offerings, and drive innovation across the drug development and commercialization lifecycle. The SME will act as a trusted advisor to senior stakeholders while enabling internal teams to design and deliver impactful solutions.
Unable to load the shape

Key Responsibilities
1. Genetic Target Identification
• Act as a domain SME to define and articulate solutions for genomic and transcriptomic data analysis supporting therapeutic target identification.
• Engage with business stakeholders to translate scientific problems into scalable analytics and AI-driven solutions.
• Design solution approaches leveraging:
Differential gene expression analysis
Variant analysis (genomics)
Biomarker discovery frameworks
Unable to load the shape

2. QSAR Modeling (Quantitative Structure-Activity Relationship)
Provide domain expertise in computational chemistry and QSAR/QSPR modeling to design AI-enabled drug discovery solutions.
Define and propose end-to-end QSAR pipelines including:
Molecular descriptor generation
Feature engineering and selection
Model development and validation

3. Geneformer Modeling, Gene Expression & Network Biology
Design solutions leveraging:
Gene expression modeling (RNA-seq, single-cell)
Transformer-based architectures for biological data




Representation learning for gene interactions
Define approaches for:
Gene expression pattern analysis
Cell-type classification
Disease vs healthy state prediction
Architect AI-driven network biology solutions to model:
Gene regulatory networks
Pathway interactions
Systems biology insights
Unable to load the shape

4. Real World Evidence (RWE) & Clinical Data Analytics
Analyze real-world datasets (EHR, claims, registries) to support evidence generation.
Design and execute observational studies, cohort studies, and comparative effectiveness research.
Apply statistical models for outcomes analysis, survival analysis, and risk modeling.
Unable to load the shape

5. Omics Data Management
Handle large-scale omics datasets (NGS, proteomics, metabolomics).
Design and maintain data pipelines for ingestion, processing, and harmonization.
Ensure data quality, governance, and reproducibility.
Work with structured and unstructured biological datasets.

6. Data Engineering & Cloud Platforms
Work extensively with Databricks, Snowflake, and Google Cloud Platform (GCP).
Build and optimize ETL pipelines and data workflows for large-scale biological data.
Use distributed computing frameworks such as Spark for scalable data processing.
Collaborate with data engineers for cloud-native architecture and deployment.
Unable to load the shape

Data-Driven Innovation
Partner with data engineers and analytics teams to design dashboards,



predictive models, and GenAI/Agentic AI solutions supporting both commercial and R&D; use cases.
Shape innovative offerings for functions like medical affairs insights, pharmacovigilance automation, and evidence generation.
Guide strategies for harmonizing structured and unstructured data (e.g., publications, trial protocols, regulatory submissions) for actionable insights.

Stakeholder Engagement & Business Development
Lead requirement workshops, solution strategy discussions, and roadmap design with client stakeholders.
Collaborate with sales and business development teams by providing SME support in pursuits, RFP responses, and client presentations.
Act as a bridge between client needs and technical delivery teams to ensure alignment on business value.
Unable to load the shape

Required Skills and Experience:
8-10 years of experience in Life Sciences industry consulting, commercial analytics, or R&D; strategy.
Strong understanding of pharma/biotech value chain, spanning R&D;, clinical, regulatory, market access, and commercial operations.
Hands-on experience with healthcare data sources: claims, EHR, registries, RWD, RWE, genomics, clinical trial data.
Familiarity with AI/ML applications in drug discovery, clinical development, and commercial effectiveness.
Ability to engage senior stakeholders (brand teams, R&D; heads, medical affairs, market access leaders) and influence solution adoption.
Excellent communication, presentation, and thought leadership skills.
Unable to load the shape

Preferred Qualifications:
Bachelor’s/Master’s degree or equivalent in Engineering, Business, Life Sciences, Data Science, or related fields.
Industry knowledge in diagnostics, med device and clinical research.

📌 Life sciences (Bengaluru)
🏢 Tredence
📍 Bengaluru