Capgemin - Regulatory Lead - Bioinformatics/Analytics (9-14 yrs)

Mandatory :

Key Responsibilities :

Regulatory Strategy and Guidance :

- Develop and implement regulatory strategies for product development, approval, and post-market activities.

- Provide expert advice on regulatory requirements and pathways for various markets (e.g., FDA, EMA, Health Canada, etc.).

Regulatory Submissions :

- Prepare and review regulatory submissions, including INDs, NDAs, CTAs, 510(k)s, PMAs, and other regulatory documents.

- Ensure submissions are complete, accurate, and aligned with regulatory guidelines and requirements.

Compliance Management :

- Monitor and interpret regulatory changes and updates, ensuring the organization remains compliant with current regulations.

- Assist in developing and implementing compliance programs and practices.

Interaction with Regulatory Agencies :

- Serve as the primary point of contact with regulatory authorities during submissions, inspections, and audits.

- Facilitate communication between the organization and regulatory agencies to resolve compliance issues.

Documentation and Reporting :

- Maintain accurate and comprehensive records of regulatory activities, submissions, and communications.

- Prepare regulatory reports and documentation required for compliance and reporting purposes.

Training and Support :

- Provide training and support to internal teams on regulatory requirements and compliance best practices.

- Offer guidance on regulatory aspects of product development, labeling, marketing, and manufacturing.

Risk Management :

- Identify and assess regulatory risks and develop strategies to mitigate potential issues.

- Address and resolve any regulatory challenges or concerns that arise during product lifecycle management.

Continuous Improvement :

- Stay informed about industry trends, emerging regulations, and best practices in regulatory affairs.

- Recommend and implement improvements to regulatory processes and strategies.

- Perform data analysis and interpretation to generate actionable insights for research projects.

Data Analysis and Management :

- Analyze large-scale datasets from genomics, proteomics, or high-throughput screening assays.

- Use statistical and machine learning techniques to identify patterns, correlations, and potential biomarkers.

Research and Development :

- Design and conduct in silico experiments to support drug discovery, biomarker identification, and other research objectives.

- Collaborate with experimental researchers to integrate computational findings with laboratory results.

Tool and Database Development :

- Develop and maintain computational tools, algorithms, and databases to support research activities.

- Optimize existing software and tools for improved performance and accuracy.

Collaboration and Communication :

- Work closely with interdisciplinary teams, including biologists, chemists, and data scientists, to advance research projects.

- Present research findings and methodologies to stakeholders, including scientific and non-scientific audiences.

Documentation and Reporting :

- Document research methods, results, and analysis in detailed reports and scientific publications.

- Ensure all computational research complies with regulatory and quality standards.

Continuous Learning and Improvement :

- Stay updated with the latest advancements in computational biology, machine learning, and related fields.

- Continuously improve research methodologies and practices based on new knowledge and technologies.

Qualifications :

- Education : PhD in Computational Biology, Bioinformatics, Data Science, Computer Science, or a related field. Master's degree with relevant experience may also be considered.

- Experience : Proven experience in computational research, data analysis, or bioinformatics. Familiarity with drug discovery processes or biotechnology is advantageous.

Skills :

- Proficiency in programming languages such as Python, R, or MATLAB.

- Strong knowledge of computational modeling, machine learning, and statistical analysis.

- Experience with bioinformatics tools and databases (e.g., BLAST, Gene Ontology, KEGG).

- Excellent problem-solving and analytical skills.

- Strong written and verbal communication abilities.