Information Technology and Services
Any Graduation Degree
07 Sep 2020
- Predictive Analytics
- Data Science
- Financial Modeling
- Direct Mail
- This role within our data analytics team is responsible for contributing to the design, implementation and maintenance of new software algorithms of our platform that we delivers to B2C financial services industry.
- The successful candidate will love collaborating on high energy, Agile teams and working with product team to build new photogrammetry analytics tools.
- Graduate degree in Machine Learning, Computer Science, or Artificial Intelligence (current or completed)
- Experience working in predictive models, data models, financial models, and machine learning models.
- Strong applied math skills in statistics, modeling, computations, and financial models.
- Experience with B2C business data analysis, B2C financial data analysis and demographic data analytics.
- Experience with machine learning, regression, nural networks, trees, nave bayes, and related applications.
- Experience with developing software in R, and Python
Nice To Have
- Experience with direct mail marketing models is a plus
- Experience with analyzing web log data is a plus
- Experience with analyzing credit data elements like FICO Score, payment history, credit amount, credit balances, inquiries, etc. is a plus
- Experience with analyzing demographic data elements - Gender, Marital Status, Age, Estimated Income, Homeownership, Dwelling Type, etc.
- Experience deploying solutions to AWS or cloud services a plus.
- You have strong contributions in development communities, open source projects, or forums.
- You will be working on developing new predictive models, machine learning models, analytical models, data models. Statistical models, financial models and related applications.
- You'll define technical roadmap and work on cutting edge technology in predictive modeling, machine learning and neural networks.
- You will develop predictive models, including a linear regression model other models like ML neural nets, trees, or Na- ve Bayesian.
- You'll benchmark algorithms based on speed, accuracy and robustness.
- You'll implement efficient data structures to enable faster machine learning models.