Senior Machine Learning Engineer

February 25

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tvScientific

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tvScientific is a company that provides a cutting-edge platform for connected TV (CTV) advertising. It offers solutions for precise targeting, premium inventory access, and total control over advertising campaigns. Advertisers can leverage thousands of unique behavioral targeting segments to reach their ideal customers. The platform emphasizes radical transparency and delivers real-time insights into campaign performance, targeting, and attribution. tvScientific aims to optimize advertising spend by focusing on tangible business outcomes and offering tools for real-time optimization and brand safety. It serves various industries including retail, mobile games, fintech, and insurance, and caters to consumer brands of all sizes.

51 - 200 employees

📱 Media

💰 $20M Series A on 2022-04

📋 Description

• tvScientific is looking for a Senior Machine Learning Engineer to join our growing team! • You'll be working with a distributed engineering team on our Connected TV ad-buying platform, as we scale our Data Science practice. • We’re building data science tools for constructing ad campaigns from content to timing to scheduling to bid optimization. • Our self-managed platform makes it easy to buy, optimize, and prove the value of TV advertising. • In this role, you’ll lead small project teams, provide direction, and keep stakeholders informed. • You’ll have the autonomy to determine key milestones and provide updates and check-ins to relevant teams and partners.

🎯 Requirements

• Proficiency in writing and reviewing production-level Python code. • Deep understanding of statistics and its application in machine learning. • Strong communication and writing skills. • Desire to excel in a fast-paced Series B startup environment, embracing uncertainty and driving innovation through experimentation and iteration. • Experience in adtech or connected TV (CTV) environments. • Proficiency with big data technologies such as Scala, Apache Spark, Apache Beam, and AWS Athena. • Experience with experimental design and A/B testing methodologies. • Previous work with bandit algorithms and reinforcement learning techniques. • Teaching experience. • Systems programming experience in Zig. • MLOps/KubeFlow. • Causal inference.

🏖️ Benefits

• Full health, dental, and vision insurance - up to 95% funded by the company for employees. • Employee stock option program. • Company-sponsored retirement plan with a matching contribution program. • 12 annual paid holidays (including 2 flexible days). • Generous PTO policy (get your work done and take the time you need). • A remote-first environment that allows employees flexibility to work from most places in the US.