Senior Research Computational Social Scientist

Job Description

1. Oversee the implementation of applying deep learning, artificial intelligence, and machine learning
   methods to the study of risk- and engagement-related behaviors.
2. Analyze and interpret large datasets to identify causal relationships between variables by applying
   statistical methods.
3. Develop or identify the computational methods appropriate to execute needed research.
4. Collaborate with experts from different fields on interdisciplinary research.
5. Communicate findings in written reports and in oral presentations to company executives.
6. Design solutions using deep learning architectures and train deep learning models to predict actuarial
   variables such as claims frequency, severity, and loss cost using Pytorch.
7. Design and execute data evaluations to explore potential future improvements in current products.
8. Research and explore the use of machine learning and deep learning techniques to produce potential
   new products that identify risks for insurers.
9. Provide support to Data Engineering for the deployment of products.
10. Develop the code of new products and add-on to current ones using Pytorch, SQL, Scitkit-Learn, and
    PySpark.
11. Implement techniques to improve the efficiency of the products’ pipeline by performing feature
    selection and feature engineering techniques.
12. Implement fairness evaluations on deep learning and machine learning models in Python.

May telecommute.

Minimum Requirements:

Master’s (or foreign educ. equiv.) Degree in Computer Science, Statistics, Economics, or related quantitative field
plus three (3) years’ experience in the job offered or related.

Special Skill Requirements: 

1. Applying statistical methods and analyze data.
2. Using Python, R, Pandas, and SQL.
3. Designing solutions using deep learning architectures.
4. Applying artificial intelligence, deep learning, and advanced machine learning techniques to behavioral science problems.
5. Using deep learning frameworks, including Scikit-Learn, H2O, NumPy, Pytorch and Tensorflow.
6. Working with large datasets.
7. Performing research on the application of machine learning and deep learning to social sciences.
8. Collaborating with experts from diverse fields to facilitate interdisciplinary research and foster connections between computational and social science communities.
9. Conducting fairness evaluations and corrections using deep learning and machine learning models.
10. Performing feature selection and feature engineering techniques.

To apply for this role, please send resume to careers@pinpoint.ai