POD Lead – Computational Biology & Life Sciences

ZettaMine Labs Pvt. Ltd. is a technology-focused organization seeking to leverage advanced expertise in artificial intelligence and machine learning. The company is currently recruiting for roles such as LLM Engineering Expert, indicating an interest in developing large language models and related technologies. While the specific industry and website are not provided, the job title suggests involvement in AI research or product development. ZettaMine Labs appears to be positioned at the intersection of innovation and practical application, aiming to drive progress through specialized technical talent. The company's focus on LLM engineering highlights its potential engagement with natural language processing, data analysis, and scalable AI solutions. Without additional information, the exact scope of operations remains inferred based on the job title provided.

Hello, Greetings from ZettaMine!!! Freelancing Opportunity: We are hiring a POD Lead – Scientific Computing Expert (Life Sciences PhD) for a short-term Frontier AI Research, Computational Biology & Scientific Evaluation project across the following locations. Eligible Locations: 🇧🇩 Bangladesh | 🇧🇷 Brazil | 🇨🇴 Colombia | 🇪🇬 Egypt | 🇬🇭 Ghana | Indonesia | 🇳🇬 Nigeria | 🇹🇷 Turkey Role: POD Lead – Scientific Computing Expert (Life Sciences PhD) Educational Eligibility: PhD, postdoctoral experience, or equivalent research experience in Life Sciences, Medicine, or a closely related discipline, with strong computational and scientific programming expertise. Mode: Remote Start: Immediate Mandatory Eligibility Criteria Technical Qualification: PhD, postdoctoral, or equivalent research experience in Life Sciences, Medicine, or a closely related field, with demonstrated computational and scientific programming expertise. Strong scientific programming skills, particularly in Python. Previous experience working as a POD Lead in similar projects, leading and mentoring technical trainers. Expertise in at least one area, such as Bioinformatics, Computational Genomics, Systems Biology, Computational Neuroscience, Biostatistics, Computational Drug Discovery, Structural Biology, Protein Engineering, or Computational Microbiology. Ability to independently validate scientific reasoning, research methodologies, computational workflows, and analytical outputs. Experience reviewing scientific datasets, analysis pipelines, reference solutions, computational environments, and automated tests. Strong understanding of scientific accuracy, statistical methods, reproducibility, data validation, and computational evaluation. Ability to identify incorrect assumptions, analytical errors, unreliable results, and weaknesses in automated grading systems. Strong analytical and problem-solving skills, with the ability to provide clear, actionable technical feedback. Experience mentoring trainers, reviewing deliverables, maintaining quality standards, and ensuring timely project delivery. Preferred Qualifications: Familiarity with bioinformatics tools such as Samtools, BCFtools, GATK, BWA, STAR, Salmon, DESeq2, edgeR, Seurat, Scanpy, or Biopython. Familiarity with scientific workflow tools such as Nextflow, Snakemake, Docker, Conda, Git, and high-performance computing (HPC) environments. Knowledge of computational drug discovery and structural biology tools such as RDKit, OpenMM, GROMACS, AutoDock Vina, or PyMOL. Familiarity with medical imaging, clinical research, biostatistics, or biomedical data analysis tools. Experience designing scientific benchmarks, research challenges, or computational evaluation tasks. Prior experience in AI training, AI evaluation, or scientific research projects is an added advantage. Availability Minimum 40 hours per week. Availability to work at least 8 hours per day. Minimum 4 hours of overlap with PST business hours. Immediate availability for a 3-week contract assignment. What You’ll Work On Lead and mentor a pod of approximately 5–10 technical trainers developing realistic, terminal-based scientific tasks for a frontier AI training and evaluation initiative. Design challenging, multi-step scientific workflows across computational biology, bioinformatics, medicine, and life sciences. Review scientific tasks end to end, including problem statements, biological and clinical datasets, reference files, computational pipelines, environments, reference solutions, and automated tests. Ensure tasks reflect authentic scientific research and clinical analysis workflows that require meaningful computational reasoning. Validate scientific methods, statistical assumptions, calculations, code, intermediate outputs, and final results for accuracy and reproducibility. Verify that computational pipelines use appropriate tools, reference databases, genome builds, annotation versions, and software dependencies. Evaluate outputs such as variant calls, differential gene expression, cluster assignments, protein structure metrics, survival estimates, model performance, medical image segmentation, and pharmacokinetic parameters. Review automated graders to ensure they assess scientific correctness and analytical quality rather than merely checking output formatting. Ensure tasks are reproducible and executable in controlled computational environments, with appropriate testing and validation procedures. Identify flawed scientific assumptions, incorrect calculations, unreliable tests, hard-coded outputs, and other evaluation weaknesses. Ensure datasets are appropriately licensed, public or synthetic, and de-identified, with no protected health information or unauthorized restricted-access data. Screen tasks for biosafety and dual-use concerns and escalate potentially harmful biological content when necessary. Provide actionable written feedback, track revisions, and ensure quality issues are resolved before task approval. Onboard trainers, allocate tasks, monitor productivity, resolve technical blockers, and maintain consistent delivery standards. Maintain documentation covering task guidelines, recurring issues, quality expectations, and best practices. Offer Details Employment Type: Contractor assignment (no medical or paid leave). Contract Duration: 3 weeks. Expected Start: Immediate / next available project start. Working Hours: Minimum 40 hours per week, with at least 4 hours of overlap with PST. Interested Candidates: Please share your updated CV and LinkedIn profile at md.afreen@zettamine.com and complete the registration and assessment process using the links above. Thanks & Regards, Saranya M (Afreen) ZettaMine Show more

The POD Lead – Computational Biology & Life Sciences oversees the integration of computational methods in biological research. This role involves leading projects that apply advanced data analysis to life sciences challenges.