Overview

Placed, the market leader in location-driven insights and mobile ad intelligence, is looking for a Data Scientist (NLP) to join our Inference team. You will have the opportunity to research, design, and implement (in Python) models that pull signal from a variety of data sets with varying attributes. The quality of data will vary from robust and broad to sparse and accurate. Alone these data sets have very little value, but when stitched together they act as a tuning fork that finds precision in a swarm of noise.

You’ll find an exciting greenfield opportunity to apply both your professional experience and advanced education to solve complex problems in the mobile location space, working with disparate data sources to stitch together precise results that will directly benefit our customers.

As a Data Scientist at Placed, you will:

Lead development and deployment of textual information processing algorithms
Procure and integrate heterogeneous, multilingual data sets
Build key metrics to measure data heath and quality
Manage in-house and crowd-sourced ground truth generation
Develop internal tools for monitoring and investigation purposes

Company:

Placed

Qualifications:

Our ideal candidate will have:

PhD in Computer Science, Linguistics, Electronic Engineering, Mathematics, Statistics, or related field
Direct experience with text categorization, statistical machine translation, and general textual data mining
Proficiency in at least one scripting language, such as Ruby or Python
Experiences with large scale data processing (e.g. Hadoop and Spark) and NoSQL databases (e.g. MongoDB) a plus
3+ years in an industrial production environment a plus

Educational level:

Ph. D.

Level of experience (years):

Mid Career (2+ years of experience)

How to apply:

Please mention NLP People as a source when applying

https://boards.greenhouse.io/placed/jobs/204439?t=3t8rif#.V37M-mSriko

Tagged as: , , , , , ,

About Placed

Placed is a location-driven insights and mobile ad intelligence platform providing reports on consumers offline behaviors.