Applied Scientist II
$96,840–$177,540 year
On-siteToronto, Ontario, Canada
Job Summary
Develop algorithms for real-time bidding platforms, managing deep learning training and inference at scale with millisecond latency and high QPS requirements. Own end-to-end data-focused products from ideation through productionization, addressing challenges like sparse labels, large output spaces, and unreliable ground truth. Collaborate with engineering teams to design, research, and deliver solutions using first principles for embedded models in always-on production systems. Lead others by proactively advocating for team improvements and driving technical excellence across distributed, global teams spanning multiple time zones.
Required Qualifications
- BS/MS in Mathematics, Computer Science, Statistics, or a related quantitative field
- 3+ years of full-time industry experience
- PhD with 2+ years of full-time industry experience
- Experience in deep learning
- Proficient in Python
- Proficient in Scala
- Proficient in SQL
- Hands-on experience with Spark
- Hands-on experience with EMR
- Hands-on experience with Ray
- Proficient in software engineering concepts like containerization
- Proficient in software engineering concepts like version control
- Experience with ads
- Experience with recommendation systems
- Experience with KPI optimization modeling
- Experience with large-scale advertising platforms
- Experience with lift modeling
- Experience with conversion lift
- Experience with brand lift
- Ontario residents only
Desired Qualifications
- Pytoch preferred
- Experience running heavy workloads on a distributed computing cluster (especially EMR or Databricks)
- Leveraging technologies like Spark to work with large datasets is preferred
- Proficient in software engineering concepts like containerization and version control is preferred
- Experience with ads, recommendation systems or KPI optimization modeling is a plus
- Large-scale advertising platforms and lift modeling (conversion lift, brand lift) experience strongly preferred
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