DESCRIPTION:

Duties: Identify strategic and operational challenges that can be solved through data, and apply machine learning and deep learning techniques to solve financial problems around payment optimization and recommendation. Pose identified problems in formats conducive for quantitative modelling. Apply descriptive and predictive models to address quantified business problems, including linear and non-linear time series techniques for time series prediction. Access and query various databases and data sources to create data sets required for predictive and descriptive analytics. Work on highly confidential data assets and perform exploratory analysis to check for missing elements and outliers to establish data integrity. Combine business understanding with theoretical knowledge to augment available data by performing feature engineering. Identify evaluation metrics to measure performance of models. Build both batch and real-time model prediction pipelines and work with data engineers to address scalability issues in testing and production environments for machine learning deployment platforms. Collaborate with multiple partner teams such as Business Management, Technology, Product Management, and Compliance to deploy solutions into production. Transform the outcomes of machine learning models into metrics of business impacts to enable accurate assessment of the risks involved and explain complex concepts to senior management and stakeholders.

QUALIFICATIONS:

Minimum education and experience required: Master’s degree in Quantitative Methods in Social Sciences, Data Science, Financial Engineering, or related field of study plus 2 years of experience in the job offered or as Applied AI/ML, Research Associate, Data Analytics Assistant Manager, or related occupation.

Skills Required: Requires 2 years of experience in the following: Python; SQL; Hadoop; Pyspark; Tableau; Machine Learning Classification; linear and logistic regression; random forest algorithm; XGBoost; any dimensionality reduction techniques such as PCA, SVD, or T-SNE; statistical simulation; hypothesis testing; bootstrapping. Requires 1 year of experience in the following: R; Pytorch; Tensorflow; Shell Scripting; AWS Cloud Services; Hive; Jupyter Lab; any time series techniques such as ARIMA; GLM; GBM Ensemble methods; Kmeans Clustering; Hierarchical Clustering; deterministic and stochastic optimization; linear algebra; multivariate calculus.

Job Location: 383 Madison Ave., New York, NY 10179. Telecommuting permitted up to 40% of the week.

Full-Time. Salary:  $180,250 - $195,000 per year.