  Occupational data mining and analysis is an important task in understanding
today's industry and job market. Various machine learning techniques are
proposed and gradually deployed to improve companies' operations for upstream
tasks, such as employee churn prediction, career trajectory modelling and
automated interview. Job titles analysis and embedding, as the fundamental
building blocks, are crucial upstream tasks to address these occupational data
mining and analysis problems. In this work, we present the Industrial and
Professional Occupations Dataset (IPOD), which consists of over 190,000 job
titles crawled from over 56,000 profiles from Linkedin. We also illustrate the
usefulness of IPOD by addressing two challenging upstream tasks, including: (i)
proposing Title2vec, a contextual job title vector representation using a
bidirectional Language Model (biLM) approach; and (ii) addressing the important
occupational Named Entity Recognition problem using Conditional Random Fields
(CRF) and bidirectional Long Short-Term Memory with CRF (LSTM-CRF). Both CRF
and LSTM-CRF outperform human and baselines in both exact-match accuracy and F1
scores. The dataset and pre-trained embeddings are available at
https://www.github.com/junhua/ipod.
