Apprentice Leader - Pharma

Job Title: Apprentice Leader – Pharma

Experience: 6–10 years

Location: Bangalore (Onsite)

Industry: Pharmaceutical / Biotechnology

Employment Type: Full-Time

Ideal Candidate Profile

We are looking for candidates who combine strong research credentials with the agility to transition into a consulting and analytics environment. The ideal profile is one of the following, in order of preference:

1. Research/Academia + Industry Experience

Candidates who have built a strong foundation in life sciences research through postdoctoral work, national research institutes, or internationally recognised academic institutions and have subsequently applied that expertise in an industry setting. This combination of scientific rigour and real-world execution is highly valued.

2. Deep Research Background with Willingness to Transition into Industry

Candidates coming directly from research or academic environments are strongly encouraged to apply, provided they demonstrate a genuine willingness to move into industry and critically, to learn, unlearn and re-learn as situations require it. Consulting and decision-science environments require a different operating model from academia: faster cycles, stakeholder-driven outputs, and applied impact over theoretical depth. We value intellectual curiosity and adaptability over prior industry tenure.

In both cases, we welcome applications from candidates with backgrounds at globally recognised research institutions including but not limited to Indian institutes such as CSIR, NCBS, TIFR, IISc, and IITs, as well as internationally renowned universities and research centres worldwide. Publication record, research leadership, and scientific rigour are weighted equally to industry experience.

Role Overview

Our client – one of the largest data science companies – is seeking to hire a Functional Consultant – Life Sciences with 6–10 years of experience in life sciences research from academia, research institutes, or industry. Postdoctoral or equivalent research-track experience at a reputed institution is strongly valued. The ideal candidate will have a strong foundation in managing scientific research projects, coordinating cross-functional teams, and collaborating with global stakeholders across geographies. Demonstrated experience in project execution, scientific communication, and data-driven decision-making is essential.

The ideal candidate will embrace the Company's Decision Sciences Lifecycle, leveraging advanced analytics, machine learning, and statistical modeling to build data-driven solutions within a global delivery model. They are self-driven, take ownership of complex problems, collaborate across disciplines, and can translate rigorous research into actionable insights. Experience mentoring junior researchers or leading a lab team is valued.

Functional Domain Requirements

Candidates must have hands-on experience or exposure to at least 1 of the 7 following functional domains:

Genomics Data Analysis & Interpretation:

• Proficiency in analyzing high-throughput genomic data, including nextgeneration sequencing (NGS) datasets (e.g., whole-genome sequencing, exome sequencing, RNA-seq).
• Practical knowledge of NGS workflows and tools such as GATK, STAR, DESeq2, or Seurat for data processing, differential expression analysis, or single-cell RNA-seq analysis.
• Experience with genomic data processing pipelines, including alignment, variant calling, and annotation using tools like GATK, BWA, or STAR.
• Familiarity with genomic databases (e.g., Ensembl, UCSC Genome Browser, dbSNP) and standards for genomic data (e.g., VCF, BAM).
• Knowledge of statistical methods for genomic data analysis, such as differential expression analysis, GWAS, or eQTL mapping.
• Expertise in interpreting genomic findings to support precision medicine, biomarker discovery, or therapeutic target identification.

Multiomics Integration & Systems Biology:

• Experience in integrating multiomics datasets (e.g., genomics, transcriptomics, proteomics, metabolomics) to build comprehensive molecular profiles for discovery of biomarkers or therapeutic targets.
• Proficiency in systems biology approaches to model biological networks, pathways, and interactions using tools like Cytoscape or Pathway Commons.
• Knowledge of machine learning and statistical modeling for multiomics data integration and predictive analytics.
• Familiarity with multiomics applications in drug discovery, disease stratification, or personalized medicine.
• Understanding of data harmonization techniques to address batch effects and cross-platform variability in multiomics studies.

Bioinformatics and Computational Genomics:

• Expertise in developing and implementing bioinformatics pipelines for processing and analyzing large-scale omics datasets, including NGS workflows.
• Practical knowledge of bioinformatics tools such as GATK, STAR, DESeq2, or Seurat for genomic and transcriptomic data analysis.
• Proficiency in programming languages (e.g., Python, R, Perl) and bioinformatics tools (e.g., Bioconductor, BEDTools, SAMtools).
• Experience with cloud-based platforms (e.g., AWS, Google Cloud, Azure) for scalable genomic data analysis and storage.
• Knowledge of computational methods for variant prioritization, functional annotation, and pathogenicity prediction.
• Familiarity with containerization (e.g., Docker, Singularity) and workflow management systems (e.g., Nextflow, Snakemake) for reproducible research.

Epidemiological Study Design & Analysis:

• Expertise in designing and conducting epidemiological studies, including cohort, case-control, cross-sectional, and longitudinal studies.
• Proficiency in statistical methods for epidemiological data analysis, such as regression modeling, survival analysis, and propensity score matching.
• Experience with tools like SAS, R, or Stata for analyzing epidemiological datasets and generating actionable insights.
• Knowledge of bias identification, confounding adjustment, and causal inference methodologies in observational studies.
• Familiarity with regulatory requirements (e.g., FDA, EMA) for epidemiological data used in drug safety and efficacy evaluations.

Clinical Trial Data Management & Analytics:

• Proficient in managing clinical trial data lifecycles—from data collection and validation to statistical analysis and reporting.
• Experience with standards such as CDISC, SDTM, ADaM, MedDRA, and tools like SAS, R, or Python.
• Understanding of regulatory compliance (e.g., FDA, EMA) and patient safety data requirements.
• Familiarity with clinical trial design, including adaptive trials, randomization, and endpoint analysis.
• Knowledge of real-world evidence (RWE) integration and post-market surveillance data analytics.
• Proficiency in monitoring and analyzing adverse event data, signal detection, and risk management planning.
• Experience with pharmacovigilance systems (e.g., Argus, ARISg) and safety database management.
• Familiarity with post-marketing safety studies and compliance with global pharmacovigilance regulations.

RWE Epidemiology/HEOR and Observational Research:

• Expertise in designing and executing epidemiological studies to generate real-world evidence (RWE), including natural history of disease, population characterization, treatment patterns, unmet needs, external comparators, clinical outcome benchmarking, and comparative safety/effectiveness research.
• Experience developing and managing study protocols, statistical analysis plans (SAPs), and study reports under scientific oversight to address priority RWE research questions.
• Proficiency in constructing cohorts using real-world data (RWD) sources (e.g., claims, EHRs, patient-reported outcomes, registries) and evaluating key variables, including diagnosis and procedure codes, with validation study planning.
• Knowledge of observational research methods (primary and secondary data collection) and biostatistics for descriptive and comparative analyses.
• Experience contributing to regulatory documents, publications, white papers, abstracts, or manuscripts for external dissemination of observational research results.
• Record of coauthoring scientific publications demonstrating expertise in observational study design, analysis, and interpretation.
• Ability to support internal and external decision-making, including rapid analyses for safety queries and responses to regulatory authorities (e.g., FDA, EMA).
• Strong project management skills to deliver studies within time, budget, and quality standards, with increasing autonomy in a matrix environment.

Drug Discovery and Development Process:

• Expertise in overseeing end-to-end drug development workflows, from discovery through clinical phases to commercialization.
• Experience in integrating cross-functional inputs from discovery, pre-clinical, clinical, and manufacturing teams to streamline development timelines.
• Knowledge of Chemistry, Manufacturing, and Controls (CMC) processes, including formulation, process development, and scale-up strategies.
• Familiarity with translational research, bridging pre-clinical findings to clinical trial design and execution.
• Proficiency in risk assessment and mitigation strategies for drug development, including managing critical path activities and ensuring regulatory readiness.
• Expertise in navigating regulatory submission processes for IND, NDA, BLA, or equivalent filings with agencies like FDA, EMA, or PMDA.
• Experience in preparing and reviewing regulatory documentation, including CMC sections, clinical study reports, and safety dossiers.

Additional Competencies

Additional competencies in data science and analytics using R, Python, or SQL, along with familiarity with Agile methodologies, JIRA, and other project management tools, are considered strong advantages. Experience with AI/ML tools, data visualization platforms (e.g., Tableau, Power BI), and working in cross-disciplinary environments will be a plus. Willingness to learn project management and delivery tooling in a consulting environment is sufficient - prior exposure is a bonus, not a prerequisite.