(This role will heavily focus on building machine learning models. This is NOT an engineering/data engineering position.)

BASE SALARY: $110,000 to $160,000 per year, paid bi-monthly

MUST HAVE: Experience within the performance marketing/lead gen industries. Professional experience designing, implementing, optimizing, and testing end-to-end Multi-Armed Bandit (MAB) and recommendation systems.

EXPERIENCE: A minimum of 4 years experience in a hands-on, in-the-weeds data science position.

YOUR ROLE

You will be developing deep personalization models for our users and complex optimization algorithms to bridge our customer experiences with new products/services. Your direct contribution will impact how we connect hundreds of thousands of customers to hundreds of advertisers together daily and will drive significant consumer impact while increasing revenue. 

You will play a pivotal role in the growth of our data science team and will be an instrumental resource as we continue to build a team of data scientists and machine learning engineers that can increase customer engagement and stickiness on our sites while improving the quality of the leads to our partners. 

This is an extremely hands-on and in-the-weeds Data Science role where you will be heavily immersed in the data and coding part of the solution implementation. You will design and oversee integrating state-of-the-art machine learning solutions across the company’s products. This role will be responsible for strategic planning of Machine Learning initiatives with the Product, Engineering, Performance and Business Intelligence teams, analysis of potential impact and prioritization of those projects.

SUCCESS LOOKS LIKE

• Innovate, create, and design ML solutions to various business problems such as:
• Design, implement and continuously improve Multi-Armed Bandit solutions to optimize decisions/options in place of multiple AB-tests.
• Utilizing Large Language Models in content personalization across various verticals.
• Recommendation systems to serve ads, offers, questions, etc.
• Collaborate with stakeholders across the company including but not limited to Analytics, Engineering, Product and Business Leads to improve model infrastructure, tracking and monitoring.
• Provide data science support at different project stages, including the implementation of ML solutions by collaborating with Data Engineers and MLOps.
• Being hands-on and in-the-weeds in the data, coding part of the solution implementation.

WHAT YOU NEED TO SUCCEED

• 4 years of related work experience in the field of Data Science & Machine learning.
• 2 years of experience in the Marketing/Advertising Industry.
• Solid background in Machine Learning and Statistics.
• Professional experience designing and implementing Multi-Armed Bandit (MAB) solutions and recommendation systems.
• Proficiency in SQL, Python and working with Data Science tools (e.g., git, kubernetes, Docker, etc.).
• Structure and clarify any ambiguity for the team, i.e., divide complex projects into sprints and coordinate implementation across multiple teams and individuals.
• Experience creating ML solutions within cloud services (AWS/GCP).
• Experience with ML model development lifecycle.
• Experience with data visualization (preferably Looker).
• Experience managing a team of data scientists and machine learning researchers (including career development).
• Ability to communicate clearly, think independently, provide direction, and effectively communicate with technical and non-technical stakeholders.

NICE TO HAVES

• Experience with LLMs and Deep learning models to develop business and data science solutions.

Total Rewards & Compensation

Base salary is set according to market rates for the nearest major metro and varies based on Launch Potato’s Levels Framework. Your compensation package includes a base salary, profit-sharing bonus, and competitive benefits. Launch Potato is a performance-driven company, which means once you are hired, future increases will be based on company and personal performance, not annual cost of living adjustments.