About the role
Requirements:
- We're looking for someone with 4.5-8 years of experience manipulating data sets and building statistical models, with a bachelor's/master's/PhD degree in statistics, mathematics, computer science, or another quantitative field, from any of the top-tier colleges.
- Strong problem-solving skills with an emphasis on product development.
- Excellent written and verbal communication skills for coordinating across teams.
- Good applied statistics skills such as distributions, statistical testing, and regression.
- Good scripting and programming skills in Python, PySpark, and SQL.
- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, XGBoost, decision forests, artificial neural networks, and their real-world advantages or drawbacks. Knowledge of deep learning techniques is a plus.
- Experience with common data science toolkits such as NumPy, Pandas, Scikit-learn, TensorFlow, Keras, etc.
- Experience with data visualization tools such as Tableau.
- Experience in credit, risk, or collections management (e. g., Application Scorecard, Behavior Scorecard, Collection Scorecard, Loan Pricing, Propensity Model, Cross-Sell Model) is a strong plus.
- Experience with NoSQL databases such as MongoDB, Cassandra, and HBase is desired.
- Experience with distributed data/computing tools like Map/Reduce, Hadoop, Hive, and Spark is a big plus.
- Hands-on experience with cloud computing and big data platforms (e. g., AWS S3, SageMaker, Athena, and Databricks) would be preferred.