Do you love data, technology, and problem solving?
Join us at Factual, where we’re hard at work organizing and optimizing the world’s location information.
Our team is focused on cleaning, structuring, and delivering our dataset of over 100 million places. To make our goals a reality, we end up taking on unusual problems with small, focused teams made up of highly motivated people.
Our Data team is seeking a Data Analyst to help us ensure we deliver the highest quality Global Places product on the market. In this role, you will investigate and solve complex data quality and delivery problems. You will acquire new data sources and contribute to our data processing software. You will author specifications for new tools and help manage technical projects.
In short, you’ll work on everything from experimental data science to wild west data wrangling. At Factual, we cultivate multidisciplinary engineering teams. Everyone is comfortable with wrangling data, but we expect each individual contributor to bring a unique skill or expertise to the table. You should be a fast-working, highly-focused individual who pays strong attention to detail, shows great leadership and organizational skills, and gets things done.
• You are comfortable with data analysis, wrangling, and curation
• You have a degree in a quantitative field with coursework in statistics (e.g. Math, Linguistics, Physics, Chemistry, Engineering)
• You feels at home on the command line and with text processing
• You are eager to learn new technologies and skills in data processing and analysis Baseline Skills (these are required):
• Relevant internship experience (non academic)
• Proficient with Unix commands and Ruby/Python scripting
• Familiar with regular expressions, DOM/CSS/HTML, parsing JSON, and information extraction
• Experience in reporting, analytics and databases Specialized Skills (you need expertise in at least one of the following):
• Experience with implementing machine learning pipelines
• Experience with Spark or MapReduce
• Experience with SQL and/or querying MongoDB or Solr Indexes Cover letters are greatly appreciated