Data Scientist at Zignal Labs

About Zignal Labs

Zignal was founded in 2011 to help customers instantly see conversations and stories taking place across media. Originally conceived as a tool for political campaigns accustomed to media “war room,” Zignal now helps private and public sector organizations visualize and contextualize the global digital media environment to solve some of the world’s most challenging problems.

Trusted by top global news outlets such as The New York Times and The Wall Street Journal, Zignal is frequently called upon by academic institutions and the media to provide data-driven insight into some of the most important and nuanced emerging issues of societal concern - including the COVID pandemic, dis- and misinformation, consumer safety, political polarization, healthcare, digital security, and risk management.

Fully remote, with Silicon Valley roots and team members in over 20 states, Zignal serves customers around the world. Learn more at zignallabs.com.


As a Data Scientist, in our Data Science and Innovation Labs team, you will work on data analytics and machine learning projects and will be involved in solutions from ideation through research and prototyping up to feature delivery including data quality measurements and improvements. You will rely on your Scala and Python coding skills to analyze a large amount of media data and build machine learning models and solutions. You will use Spark with Databricks, the AWS stack, including SageMaker with Ground Truth and S3, Elasticsearch, Neo4j, among your machine learning and NLP skills and apply them to several social media, news, blogs, broadcast, and other media sources to empower our users with key insights based on real-time analysis.


In this role, you will have the opportunity to:

Work as a member of our small, smart, and very nice Data Science Team.
Work closely with the Product Management Team and have direct impact on our product decisions.
Mine and analyze media data from various data sources to create and improve analytical insights, product-, and application features.
Play a key role throughout the whole feature life cycle starting with ideation, through PoC prototyping towards feature delivery, followed by continuous quality monitoring and improvements




In order to be successful in this you will need:

Masters degree in Computer Science, Mathematics or equivalent field with minimum 5 years of full-time work experience as a Data Scientist - or Bachelors degree with 8+ years of relevant experience 
Strong problem-solving skills with an emphasis on product development
Excellent written and verbal communication skills for coordinating across teams
A commitment and passion to learn and master new technologies and techniques
Experience with a variety of machine learning techniques (feature extraction, clustering algorithms, classification, ensemble methods, neural networks, etc.) and their advantages/drawbacks
Coding knowledge and experience with several languages: Scala and/or Python
Experience with efficiently measuring the quality of solutions and suggest improvements
Experience with Spark, or at least one distributed data processing tools like: Hadoop, Hive, etc.  
Familiarity with data quality measurement techniques, and processes




Plusses:

Advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
Natural language processing techniques: parts of speech tagging, shallow parsing, constituency, and dependency parsing, named entity recognition, emotion and sentiment analysis
Computer vision and image analysis techniques
Graph analytics, large-scale graph algorithms




Why join Zignal Labs?
- Competitive salary based on the work you do
- Flexible time off – work with your manager to take the time you need
- Excellent medical, dental, and vision coverage
- Paid parental leave plan
- Professional development and growth programs 
- A tight knit, collaborative, and transparent environment to help you succeed

Zignal Labs is proud to be an Equal Opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law.

 Applicants must be authorized to work in the United States for any employer. No sponsorship is available for this position.