ML NLP Engineer

Responsibilities

In addition to the above, they will work with the greater Data Science / Engineering teams on:

• Improving existing or developing new traffic segmentation algorithms and estimations of bid landscapes within each segment.
• Optimize real-time bidding strategies and auction mechanics to efficiently spend ad budgets delivering campaign targets given various constraints.
• Supporting and enhancing the existing work on health user profiling, prediction, and targeting tools.
• Contributing to projects relating to patient/physician identity for cross-device tracking, profiling and targeting.
• Supporting existing codebase for data integration and production support for our core models.

Requirements

These are the things that we'll be looking for from a candidate:

• 3+ years of NLP or relevant contextualization experience.
• Advanced knowledge of Python using Numpy & Pandas.
• Being able to optimize and speed up code.

In addition to the above, we'd like you to have exposure to:

• Sorting, search tree, binary heap, trie; Time & mem complexity of algorithms.
• Probability & Statistics-Markov processes and its stationary distributions; Stochastic matrix and properties of its eigenvalues; Bayesian inference and conjugate distributions; Two-sample hypothesis testing.
• Dimensionality reduction; Geometry of PCA and SVD; Geometry of L1 and L2 regularization (Why does L1 result in feature selection?); Decision Trees; Collaborative filtering; Thompson sampling; MCMC; Boosting, (Biases in Boosted DT); Bagging.
• Embeddings; Encoders; Drop-out; CNN, RNN; Internal covariate shift.