Job Title: Lifesciences Domain SME

Location: India (Bangalore/Pune/Hyderabad/Gurgaon)

Job Type: Full-Time

Job Summary:

We are seeking a highly skilled skilled 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.

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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.

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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.

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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 (Bangalore Urban)
🏢 Tredence
📍 Bangalore Urban