Senior Applied Scientist, Ads ML & ROAS

Socket.dev; Chicago (IL); On-site; USD 175,000 - 200,000; Full time

Job summary

Launch Potato is seeking an experienced Data Scientist to own the full data science engine for a priority vertical, driving ROAS from problem framing to model deployment and live performance. You will collaborate with stakeholders, hand off ML engineering last mile, and stay engaged through deployment and monitoring.

Initial focus on Insurance and Advertiser Quality, with scope broadening over time, leveraging advanced ML techniques and data-driven decision making to optimize media efficiency.

Qualifications

• Proven experience in digital marketing, performance marketing, or the leadgen industry.
• Building adtech algorithms and supporting user acquisition or paid media modeling (highly desired)
• Strong modeling fundamentals: build models that drive business impact
• Multi-year, hands-on experience building and deploying ML solutions in the AWS cloud
• Hands-on experience across core technique areas: multi-armed bandit / reinforcement learning, recommendation and ranking systems, funnel and monetization optimization, LTV modeling
• Expert Python and SQL

Responsibilities

• Own the full data science engine for a priority vertical, from business problem to deployed model to live ROAS performance.
• You will start focusing on Insurance and Advertiser Quality, with scope that broadens over time, with primary metric ROAS.

Skills

Digital marketing

Performance marketing

Python

SQL

Reinforcement Learning

Bandits

Recommender systems

Looker

AWS

Tools

Looker

Job description

Launch Potato is seeking an experienced Data Scientist to own the full data science engine for a priority vertical, driving ROAS from problem framing to model deployment and live performance. You will collaborate with stakeholders, hand off ML engineering last mile, and stay engaged through deployment and monitoring.

Initial focus on Insurance and Advertiser Quality, with scope broadening over time, leveraging advanced ML techniques and data-driven decision making to optimize media efficiency.