Senior Projects Manager – Computational Biology/Bio-Computing & AI

Job Title: Senior Projects Manager – Computational Biology & AIExperience: PhD with 5–10 years post-PhD substantial hands-on experience in computational biology/bioinformatics, AI/ML, with demonstrated leadership of handling complex technical projects.

Must have proficiency in Python/ relevant scientific computing & AI frameworks, such as PyTorch, TensorFlow, and associated data science libraries. Should be experienced with large-scale data processing, distributed computing, cloud-computing, GPU-based model training, or high-performance computing, DevOps & MLOps.Education: Ph.D. in Bioinformatics/Computational Biology/Computer Science with Artificial Intelligence & Machine Learning, or a related quantitative discipline with strong understanding of machine learning, deep learning models, & modern AI architectures.Role Type: Full-time | Research, Technology Product Development & AI InnovationPosition Overview:We are seeking a highly accomplished and visionary Senior Projects Manager – Computational Biology & AI to lead the research, development, and deployment of innovative computational algorithms, large-scale machine learning models, and advanced artificial intelligence systems for multi-omics biological data.The role will focus on building next-generation AI capabilities that integrate, analyze, and interpret complex, heterogeneous, and high-dimensional biological datasets, especially on multi-omics datasets and other molecular and clinical data modalities.The successful candidate will lead multidisciplinary teams in developing scalable AI/ML architectures, multimodal foundation models, predictive algorithms, and automated interpretation engines that transform complex biological data into clinically and scientifically meaningful insights. This position combines scientific innovation, AI/ML research leadership, computational platform development, and project/program management, requiring the ability to translate complex scientific challenges into robust, scalable, and impactful AI-driven solutions.Key Responsibilities1. AI/ML Algorithm Development and InnovationLead the design and development of innovative computational algorithms and advanced machine learning models for biological data analysis, interpretation, and prediction.Develop novel approaches for extracting meaningful biological signals from complex, noisy, sparse, high-dimensional, and heterogeneous datasets.Drive research and implementation of deep learning, transformer-based architectures, graph neural networks, generative AI, self-supervised learning, contrastive learning, and other advanced AI methodologies.Establish methodologies for model training, predictive modelling, model optimization, validation, benchmarking, explanability and continuous improvement.Evaluate emerging AI technologies and translate promising research into scalable computational solutions.2. Multi-Omics Integration & Multimodal AILead the development of algorithms and AI models capable of integrating and interpreting diverse biological data types.Design advanced computational frameworks for cross-modal data integration, representation learning, feature extraction, and biological knowledge discovery.Develop unified or interoperable AI models that learn from multiple data modalities and generate integrated biological and clinical interpretations.Address challenges associated with missing modalities, batch effects, technical variability, data sparsity, population heterogeneity, and differences across platforms and experimental technologies.Explore the development of multimodal biological foundation models capable of learning generalizable representations across datasets, diseases, populations, and use cases.3. Large-Scale Machine Learning and AI Model DevelopmentArchitect, develop, and manage large-scale AI/ML models trained on extensive and diverse biological, molecular, clinical, and biomedical datasets.Lead the development of domain-specific foundation models, including models for biological sequence understanding, molecular representation learning, disease signatures, & integrated multi-omics interpretation.Build scalable data-processing and model-training pipelines to support large datasets, distributed computing, GPU-based workloads, and high-throughput analytical workflows; and optimize computational efficiency, inference performance, reproducibility, scalability, and deployment readiness..Develop reusable model architectures, training frameworks, feature libraries, and computational components that can be applied across multiple scientific and clinical applications.Establish strategies for pretraining, fine-tuning, transfer learning, domain adaptation, & continual model improvement.4. Automated Biological and Clinical Data InterpretationLead development of automated interpretation engines that convert raw or processed biological data into structured, actionable, and scientifically grounded insights.Build AI-driven workflows that integrate data quality assessment, feature extraction, model inference, evidence aggregation, interpretation, & report generation.Develop computational approaches for identifying disease-associated molecular patterns, clinically relevant biomarkers, molecular subtypes, & potential mechanisms of disease to enable automated interpretation across multiple use case.Incorporate biological knowledge bases, pathway databases, literature-derived evidence, and other curated knowledge resources to improve interpretability & contextual relevance.5. Computational Biology Research and Scientific LeadershipDefine the computational biology & AI research roadmap in alignment with organizational scientific, product, and business objectives; and translate emerging scientific discoveries & computational methodologies into practical AI capabilities.Identify high-impact novel research opportunities at the intersection of systems biology, multi-omics, machine learning, & precision medicine and lead development of novel methodologies, algorithms, prototypes, & proof-of-concept systems.Critically evaluate scientific literature, benchmark emerging methods, and establish best practices for computational research; incorporate methodological innovation, reproducible research, & publication-quality analytical standards.Collaborate with domain experts to ensure that developed models capture meaningful biological relationships rather than relying solely on statistical associations.6. Project and Program ManagementOwn end-to-end execution of complex AI and computational biology projects, from problem definition and feasibility assessment through research, development, validation, and deployment.Develop project plans, milestones, resource requirements, timelines, budgets, and measurable success criteria, and manage multiple concurrent projects and prioritize initiatives based on scientific value, technical feasibility, strategic importance, and business impact.Coordinate multidisciplinary teams comprising computational biologists, bioinformaticians, laboratory scientists, clinicians, and product teams.Establish effective project governance, risk management, technical reviews, and performance reporting processes. Anticipate scientific, data, computational, regulatory, operational risks, and develop mitigation strategies.Ensure timely delivery of robust, scalable, well-documented, and maintainable AI solutions.7. Data Engineering, DevOps, MLOps, and Platform DevelopmentGuide the design of scalable data architectures and computational pipelines for the ingestion, harmonization, transformation, and analysis of multi-omics & clinical datasets.Oversee the development of robust workflows for data preprocessing, quality control, normalization, feature engineering, and metadata management.Establish model development, deployment practices covering experiment tracking, version control, automated testing, model registries, monitoring, & reproducibility.Operationalize research models within production-grade computational platforms, APIs, analytical applications, and automated reporting systems, and promote cloud-native and distributed computing architectures where appropriate.Ensure appropriate data security, access controls, privacy protections, and governance throughout the data and model lifecycle.8. Model Validation, Explainability, and Responsible AIEstablish rigorous validation frameworks to assess model accuracy, robustness, generalizability, reproducibility, and clinical or scientific utility.Design appropriate benchmarking strategies, independent test sets, external validation studies, & prospective evaluation approaches where feasible, and evaluate model performance.Develop interpretable AI methods that help scientists & clinicians understand model predictions and their supporting evidence.Implement uncertainty quantification, out-of-distribution detection, bias assessment, & safeguards against unsupported interpretations.Work with clinical, quality, regulatory, and compliance teams to align development and validation practices with applicable requirements, particularly for intended clinical use.9. Team Leadership and Stakeholder CollaborationLead, mentor, and develop high-performing teams across computational biology, AI/ML research, bioinformatics, and data science and establish a culture of innovation, scientific integrity, technical excellence, collaboration, & accountability.Communicate scientific findings, technical strategies, project status, and business implications to senior leadership and non-technical stakeholders.Translate broad scientific and clinical requirements into clearly defined computational problems, development roadmaps, and measurable deliverables.Build cross-functional alignment among research, clinical, product, and commercial teams, and manage collaborations with all external stakeholders, where required.Fill-up the Form & upload your resume: https://forms.gle/Bfm68YhB7y9RPy5P9