Senior Research Scientist, Causal AI

Surgo combines behavioral science, AI and data science to bring precision to solutions that save and improve lives. With a laser focus on uncovering the ‘why,’ we partner with organizations and governments to help unlock some of their biggest challenges. Surgo works on major global issues from COVID in the US to reproductive health in India. Our multidisciplinary team includes development experts, data scientists, behavioral scientists, and technologists who are skilled in collecting and interpreting data on why people behave the way they do, how complex systems work, and in developing scalable tools. We are a small but diverse team with big plans.

Surgo’s Causal Machine Learning Initiative for Precision Public Health is testing applicability and scalability of applying machine learning approaches to analyze large public health data sets. We have built a suite of software and workflow tools to enable easier application of causal machine learning to help global development programs uncover complex insights and support intervention decisions.

 

Role Description:

We are looking for an exceptional, creative, and highly motivated Senior Research Scientist to bring our causal machine learning toolbox to the next level to aid the analysis of real-world behavioral data sets in global health issues and other social impact areas. You will also be working with an interdisciplinary team to increase performance of these tools with data scientists, and to advance the effectiveness and efficiency of their usage. You would be working with global health domain experts to generate causal insights in different global problems, and rapidly develop case studies using the causal discovery and inference tools and methods.

This is a senior role within our research team. This position requires proven algorithm optimization experience creatively analyzing and applying statistical methods to challenging research problems with often messy, observational-only data. This requires a passion for unraveling and defining challenging ad hoc research problems and integrating diverse methods to answer them through insights.

The ideal candidate is an experienced leader passionate in machine learning for social good, entrepreneurial in spirit, strong problem solver, and an excellent communicator to both academic and non-academic audiences. This is an exciting opportunity to contribute to the voice and influence strategy and delivery for an innovative, ambitious, and fast-growing organization.

 As Senior Research Scientist, your responsibilities will include:

  • Lead R&D activities on our causal discovery python package (a suite of modular packages for causal discovery and inference using Bayesian networks).

  • Improve the usability and usefulness of the causal discovery and inference tool through improved algorithms, interfaces and outputs with best practices in software development.

  • Support program managers and domain experts to appropriate machine learning approaches Surgo’s various quantitative workstreams.

  • Lead scientific communication to stakeholders, journals and conferences.

  • Maintain professional and technical knowledge by attending educational workshops; reviewing professional publications; establishing personal networks.

  • Creatively apply, adapt, and integrate innovative statistical and experimental methodologies to identify and resolve R&D problems.

Requirements:

  • PhD-level candidate with a background in computer science, physics, mathematics, statistical sciences, computational modeling, or other computational disciplines and at least 4+ years of machine learning research and development experience. Very experienced MS-level candidates will be considered.

  • Expertise in causal discovery methods such as structural learning algorithms of causal Bayesian networks is required.

  • Be a Python expert, including code optimization (for machine learning applications) and package development. Some subroutines are implemented in C++ so familiarity is desirable. Some knowledge of R is a bonus as much of our data science research is in R.

  • Knowledge of causal inference methods other than Bayesian network such as structural equation modeling, potential outcomes framework and quasi-experimental methods (instrumental variables, regression discontinuity, etc.) is a plus.

  • Prior development experience to bring prototype to scalable product in industry R&D environment is a plus. Prior experience in working with public health or human behavioral data sets is a plus.

  • Demonstrate knowledge of machine learning methods and optimization. Be experienced in conducting research, developing and applying research software. These may include probabilistic graph models, deep learning, boosted methods, random forests, computer vision, natural language processing, and cloud computing environments.

  • Proven track record of working with messy (rather than Kaggle-type) data.

  • Proficient visualizing and communicating results through effective slides aimed at non-technical audiences.

  • Excellent communication (written, verbal and visual presentation) skills with a demonstrable publication record

  • Willingness to travel

 

Location: Washington DC, USA (we are currently operating remotely due to COVID-19)

Compensation: Highly competitive compensation and benefits package

Travel: International and national travel (10-20%) once safe to do so

 

Our hiring philosophy:

We hire for passion and core competencies.

We look for problem solvers and lateral thinkers.

We love it if you have done different things with your time.

 

Application:

  • Interested applicants, please send your 1) resume/CV and 2) brief cover letter to Dr. Vincent Huang, Director of Data Science & AI, jobs@surgofoundation.org. Please add “Application for Senior Research Scientist -Causal AI Position” in the subject of the email.

  • Short-listed applicants will go through several steps: phone interviews with multiple team members across the organization, a take-home project, and a virtual on-site presentation with the team.