Role

SENIOR QUANTITATIVE MACHINE LEARNING LEAD Engineering

US (All Locations)

About Role

EDA with various source data to gain insight into data and clearly explain the observations. Formulate strategy of predictive models exploration and future product integration.
Exploring and developing univariate and multivariate forecasting models (AR, ARIMA, VAR, etc.)
Exploring and developing neural network-based forecasting models (examples include LSTM-based, AR-net, Neural Prophet, etc.)
Systematic benchmarking of various models/platforms
Oversee analytics dashboard design and implementation

Technical Skills

Desired Skills

Strong skills with EDA with Python or R. Strong with foundational statistical analysis, common statistical distributions.
Proficient with linear space analysis, such as PCA, SVD, dimensional reductions, explainability, etc.
Strong skills with time-series data processing, forecasting models. Traditional AR, MA, ARIMA, VAR. Familiar with neural network-based time series prediction/forecasting: gradient boosting, RNN, AR-net, etc.).
Must have strong programming skills in Python: data structures, OOP, scripting.
Familiarity with SQL database, data warehouse.
Familiarity with Docker, Dockerfile.
Familiarity with REST API, JSON structure. Python packages like FastAPI.
Familiarity with git operations
Good communication skills with team members across different time zones.

Strong statistical background, bayesian analysis, matrix exploration.
Prior experience with price predictions, cash flow forecasting, etc.
Familiarity with AWS Forecast, Fraud Detection
Understanding of neural networks underpinnings and toolkits like Pytorch or Tensorflow
Familiarity with traditional accounting methodology for quantitative forecasting
Prior startup work experience desired.