• Job Type: Officer of Administration
• Bargaining Unit:
• Regular/Temporary: Regular
• End Date if Temporary:
• Hours Per Week: 35
• Standard Work Schedule:
• Building:
• Salary Range: $120,000-$170,000

Position Summary

The AlQuraishi Lab in the Departments of Systems Biology and Computer Science at Columbia University is seeking a Scientific Program Manager with experience in technical management of machine learning projects. The Scientific Program Manager will work in a complex, multi-team effort to steer and coordinate development of next-generation machine learning models for biomolecular structure prediction. Working with the Principal Investigator (PI), the candidate will be responsible for the operation and administration of multiple projects spanning three consortia: (i) OpenFold for developing frontier models for biomolecular systems, (ii) AISB (AI Structural Biology Network) for training federated models on proprietary pharmaceutical repositories, and (iii) OpenBind for large-scale data acquisition of crystallographic datasets for training next-generation biomolecular models. This includes ensuring that scientific progress and/or deliverables occur in a timely fashion, that progress is communicated among collaborating teams within and outside of Columbia, and that frequent engagement is made with external partners in biopharma and the tech industry. The candidate will initiate and lead meetings among the scientific staff and will work closely with the PI and research team members to ensure that research studies are completed on time and within computational budget.

This is a position for a highly organized person with good management skills and includes cross-disciplinary project coordination, scientific communication, and liaising with multiple internal and external partners informally and through formal reports.

Responsibilities

Duties

• 45%: In conjunction with PI, help manage and administer multiple team projects. Oversee general operation of key projects and ensure that ongoing administrative and operational processes support the goals and objectives of the research agenda. Plan and lead routine project team meetings.
• 30%: Decompose large projects into tasks and subtasks, and assign and track responsibilities across team members, ensuring timely execution of project requirements in accordance with deadlines.
• 25%: Direct and prioritize resource allocation to ensure that research deliverables are met.

Functional Knowledge

• Strong familiarity with key components of the research program, including machine learning, computational biology, and molecular sciences.
• Experience with scientific writing and sufficient familiarity with scientific aspects of research to draft text independently.
• Project management experience, preferably experience overseeing a large, multi-component project.

Problem Solving

• Ability to troubleshoot tissue processing protocols including the isolation of viable cells of multiple lineages from disparate human tissue sites.
• Ability to troubleshoot the analysis of single-cell RNA-seq and high-dimensional flow cytometry data with computational biology collaborators.
• Ability to prioritize the distribution of tissue resources across a variety of projects in consultation with the PI.

Decision Making/Autonomy 

• Support PI by maintaining oversight of all personnel working for the program (>15 personnel).
• Maintain awareness of team engagement; identify and respond to key challenges and areas of opportunity.

Leadership

• Support PI by maintaining oversight of all personnel working for the program (>15 personnel).
• With PI, set the strategy and establish standards for hiring, performance appraisal, and competency assessment. Establish job expectations and methods for measurement of standards for all personnel.
• Maintain awareness of team engagement; identify and respond to key challenges and areas of opportunity. 

Technical Expertise

• Apply (and develop, to the extent necessary) tooling required for project management and tracking, including that of human-developed artifacts as well as computational artifacts and experiments.
• Help manage and organize sizable machine learning training runs (100s ? 1000s of GPUs) and computational experiments (petabytes of data).

Communication Skills

• Informally and formally communicate results to, and receive and distill feedback from, external partners in participating consortia.
• Independently interact with external partners (such as pharmaceutical and tech company representatives) and key project personnel on behalf of Principal Investigator.
• Write formal and informal communications and reports regarding project progress and share directly with sponsors, collaborators, and other members of the leadership team.

Minimum Qualifications

• B.S. in computer science / machine learning, computational biology, or related quantitative fields plus five years of related experience, or equivalent combination of education/experience.

Preferred Qualifications

• M.S. degree in a relevant field is strongly preferred.
• Working experience with machine learning runs and job management is strongly preferred.
• Published scholarly work in related field is preferred.

Equal Opportunity Employer / Disability / Veteran

Columbia University is committed to the hiring of qualified local residents.

About Columbia University

Columbia University is one of the world's most important centers of research and at the same time a distinctive and distinguished learning environment for undergraduates and graduate students in many scholarly and professional fields. The University recognizes the importance of its location in New York City and seeks to link its research and teaching to the vast resources of a great metropolis. It seeks to attract a diverse and international faculty and student body, to support research and teaching on global issues, and to create academic relationships with many countries and regions. It expects all areas of the university to advance knowledge and learning at the highest level and to convey the products of its efforts to the world.

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