(This role will heavily focus on building machine learning models.
This is NOT an engineering/data engineering position.
)MUST HAVE:A minimum of 4 years of hands-on experience in data science and machine learning.2+ years of experience in the performance marketing or lead generation industry.Expertise in designing, implementing, optimizing, and testing Multi-Armed Bandit (MAB) solutions and recommendation systems.Proficiency in Python, SQL, and experience with Data Science tools like Git, Docker, and Kubernetes.ROLE MISSIONYou will play a critical role in advancing Launch Potato's data science capabilities by developing and deploying machine learning solutions that power key initiatives across the business.
Your contributions will directly enhance customer personalization, optimize monetization models, and improve lead quality for our partners.
As a thought leader, you'll drive innovation by leveraging cutting-edge methodologies such as Multi-Armed Bandit solutions, recommendation systems, and LLMs to deliver measurable business outcomes.OUTCOMESMachine Learning Impact: Build scalable ML solutions that contribute to measurable improvements in company performance metrics within the first 12 months.Cross-Functional Collaboration: Actively partner with Product, Engineering, Business Intelligence, and other teams to integrate ML models into initiatives across verticals, such as Credit Cards, Insurance, and Prizes & Games.Innovation & Ownership: Identify and prioritize high-impact ML opportunities within the first three months, leading their design, implementation, and deployment by month six.Leadership & Mentorship: Serve as a thought leader by mentoring junior data scientists and contributing to the growth of Launch Potato's data science team.Scalability & Efficiency: Create containerized ML models using Docker/Kubernetes that ensure efficiency, scalability, and ease of deployment.APPLY IF YOU ALREADY HAVEExperience: Minimum 4 years experience in a hands-on, in-the-weeds data science position.Industry Expertise: A deep understanding of monetization methodologies within performance marketing and the ability to translate them into data science applications.ML Specialization: Experience building and productionizing MAB solutions, recommendation systems (content-based, collaborative filtering, hybrid), and deep learning models.Technical Skills: Expert-level Python and SQL skills, along with hands-on experience in cloud services like AWS or GCP and familiarity with the ML lifecycle.Competencies:Analytical Excellence: Proven track record of leveraging ML models to drive company-level performance metrics.Curiosity & Innovation: A passion for learning and staying updated with the latest ML/AI trends, technologies, and applications.Leadership: Ability to independently own projects, structure complex challenges, and collaborate effectively across teams.NICE TO HAVESExperience utilizing LLMs and deep learning techniques for personalization and business solutions.Proficiency in data visualization tools like Looker.A track record of contributing to the growth and roadmap of a data science team.Experience training, mentoring, or managing junior data scientists and machine learning engineers.
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