Pod Lead - Scientific Computing Expert - Life Science PHD

Zettamine Labs; United States; Remote; USD 45,000 - 65,000; Full time

Job summary

ZettaMine is seeking a POD Lead Scientific Computing Expert (Life Sciences PhD) for a remote freelancing engagement. Immediate start for a 3-week contract to guide a large pod of technical trainers in designing and validating frontier AI research tasks across computational biology, medicine, and life sciences.

Strong Python programming and leadership required. The role emphasizes developing authentic scientific workflows, rigorous validation, and reproducible pipelines, with ongoing feedback and

Qualifications

• 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 one or more areas such as Bioinformatics, Computational Genomics, Systems Biology, Computational Neuroscience, Biostatistics, Computational Drug Discovery, Structural Biology, Protein Engineering, or Computational Microbiology.

Responsibilities

• Lead and mentor a pod of approximately 510 technical trainers developing terminal-based scientific tasks.
• Design challenging, multi-step scientific workflows across computational biology, bioinformatics, medicine, and life sciences.
• Review scientific tasks end to end, including problem statements, datasets, reference files, pipelines, environments, reference solutions, and automated tests.
• Ensure tasks reflect authentic scientific research and clinical analysis workflows that require meaningful computational reasoning.
• Validate methods, statistical assumptions, calculations, code, intermediate outputs, and final results for accuracy and reproducibility.
• Verify computational pipelines use appropriate tools, reference databases, genome builds, annotation versions, and software dependencies.
• Evaluate outputs such as variant calls, differential expression, clustering, protein metrics, survival estimates, model performance, medical image segmentation, and PK parameters.
• Review automated graders to ensure they assess scientific correctness and analytical quality rather than formatting.
• Ensure datasets are licensed, public or synthetic, de-identified, with no PHI or restricted data.
• Screen tasks for biosafety and dual-use concerns and escalate harmful content when necessary.
• Provide actionable written feedback, track revisions, and ensure quality issues are resolved.
• Onboard trainers, allocate tasks, monitor productivity, resolve blockers, maintain delivery standards.
• Maintain documentation covering task guidelines, recurring issues, quality expectations, and best practices.

Skills

POD Lead

Python

Mentoring trainers

Scientific computing

Life sciences

Education

PhD or postdoctoral in Life Sciences

Postdoctoral experience

Tools

Samtools

GATK

Nextflow

Docker

Snakemake

RNA-Seq tools

Job description

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.

Location: India

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 - Freelancing

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 Youll Work On

• Lead and mentor a pod of approximately 510 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 elevate 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.

Thanks & Regards,

Karthik K

ZettaMine