Early Warning is looking for a data science team member to deliver machine learning and artificial intelligence concepts from start to finish, including understanding business problems, aggregating data, building algorithms, quantifying model value, understanding model risks, deploying models, and measuring model performance.
Requirements
- Able to write Model development technical documents.
- Experience using data visualization tools.
- Able to write production level code, which is well-written and explainable.
- SAS, Python, SQL or R programming training or experience.
- Experience applying various machine learning techniques and understanding the key parameters that affect their performance.
- Knowledge of ML algorithms
- Experience using ML-related libraries, such as scikit-learn, pandas, etc.
Responsibilities
- Aggregating, and exploring data
- Building, and validating algorithms
- Quantifying the value of the model to Early Warning Customers by performing simulations utilizing real world inputs
- Understanding, and articulating the model risks
- Deploying completed models to deliver business results
- Regularly measuring model accuracy, drift and performance
- End to end feature engineering - brainstorm, create, validate, down-select, etc.
Other
- Candidates responding to this posting must independently possess the eligibility to work in the United States, for any employer, at the date of hire. This position is ineligible for employment Visa sponsorship.
- Bachelor’s Degree in Engineering, Mathematics, Statistics, Computer Science, Operational Research or related field or equivalent work experience.
- A minimum of 2 years data science, engineering, mathematics, or related work/ intern/ course experience is required with Bachelor's degree or Master's degree without experience (or some internship)
- Willingness to troubleshoot system/data issues hindering the analytics environment functionality.
- Ability to effectively communicate findings from complex analyses to non-technical audiences. Ability to communicate with various levels of employees within the department and proven technical and analytical skills.
