Sr. Software Engineer, Brand Safety
PulsePoint™ is a next-gen advertising technology platform that fuses the science of programmatic targeting, distribution and optimization with the art of content marketing. Our platform is powered by terabytes of impression-level data, allowing brands to efficiently engage the right audiences at scale while helping publishers increase yield through actionable insights.
PulsePoint is committed to “Making Ads Matter” and with so many forms of digital advertising sources, it’s more critical than ever to ensure our client’s messages are seen in the right environment and aligned with their brand and target audience.
The Sr. Software Engineer, Brand Safety is integral to this process will be an critical part of the team focused on the delivery of our Inventory Quality and Brand Safety technical processes and operations. This position is ideal for you if you are passionate about data, enjoy providing actionable insights and creating innovative solutions that rely on predictive modeling and big data analytics which, in turn, informs every layer of our product stack.
Ideal Background and Qualifications:
- Previous experience at "Brand Safety/Inventory Quality" companies (e.g. IAS, WhiteOps, Spider.io, MOAT, Zvelo, Forensiq) highly preferred.
- Experience monitoring user behavior in response to external triggers or other fail proof tests of user authenticity. Ideally, experience in implementing browser and device level measurement/analysis frameworks for metrics such as viewability and bot/human classification.
- A strong background in network protocols and online security standards.
- Experience with client-side cross-browser test driven development and test automation using tools like webdriver, mocha, karma, and chai.
- Awareness of detection methods of fraudulent traffic/content - pop-ups/pop-unders, ad injection, traffic from toolbars/hijacked browsers, video & mobile fraud, etc.
- Previous experience in data science a plus - predictive model design, feature engineering, and design/deployment of applied machine learning models.
- Hands on experience in big data processing technologies like Hadoop, Hive, and Spark.
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