Featured Job

Sign In
 [New User? Sign Up]
Mobile Version

Data Engineer - Machine Learning

SnapRapid


Location:
London, Camden W1T 6DU
Date:
05/05/2017
Job Type:
Employee
Job Status:
Full Time
SnapRapid
  •  
  • Save Ad
  • Email Friend
  • Print
  • Research Salary

Job Details

About Us

SnapRapid is a specialist sport, style and entertainment digital media analytics company, offering unique, patent pending, solutions which enable brands, celebrities and influencers understand their cultural significance through social media.

We have developed complex technologies for dissecting and evaluating context in conversations and brand value in visual content, enabling us to offer a complete Brand Equity understanding.

It’s what we call Cultural Key.

Required  Skills 

  Background in Computer Science

  You have built and operated data and analytic pipelines for real customers in production systems

  You are fluent with 4+ years in at least one statically typed language and one dynamically typed (JVM & otherwise) Scala and Python are preferred 

  You enjoy wrangling huge amounts of data and exploring new data sets

  You tend to obsess over code / system / model simplicity and performance

  You have good understanding of algorithms and data structures 

  You want to work in a fast, high growth startup environment 

Also Important 

  You are deeply familiar with Spark, Kafka, Cassandra, Elastic Search, and AWS 

  Have used a functional programming language (Scala, Haskell, Ocaml, etc)

  In addition to data pipelines, you’re also familiar with end-end machine learning workflows. Such as feature engineering/inclusion/transformations, model selection, model serving, bandit testing, model persistence and updating. 

  You’ve built applications that run on AWS

  You have opinions about Lambda architecture / Kappa architecture

  You’ve built your own data pipelines from scratch, know what goes wrong, and have ideas for how to fix it

  Background in Machine Learning or Distributed Systems

  Experience with NLP 

  Have worked with processing (OLAP), storing (OLTP), or has done analysis of social networks.