Data Scientist, Growth and Retention

Company Overview:

Crazy Maple Studio is a fast-growing company based in the Bay Area and Los Angeles, focusing on mobile gaming, reading, and streaming. We are proud to have a global team that drives our innovation and success across diverse markets.

Our flagship platform, ReelShort, is the #1 vertical streaming app in the U.S., with over 3 million DAU, delivering bite-sized episodic content that keeps viewers hooked. Other standout products include Chapters, where users shape immersive stories across drama, fantasy, and romance, and My Fiction, a platform for consuming and creating serialized romance.

Position Overview:
We are looking for a Data Scientist to drive growth and retention strategies for our gaming and streaming products. This role analyzes user behavior, develops predictive models, and optimizes engagement through data-driven insights. Collaborating with product, marketing, and content teams, the ideal candidate will have strong analytical skills, experience in gaming or streaming platforms, and proficiency in Python, R, or SQL. They will translate complex data into actionable strategies, enhancing user retention. This on-site role in Sunnyvale, CA (5 days/week) is perfect for a proactive leader passionate about leveraging data to shape the future of entertainment.

Requirements

• Deliver actionable insights and recommendations to improve user engagement and retention for different products.
• Collaborate with cross-functional teams, including product, marketing, and content, to identify opportunities for optimization and innovation.
• Develop and implement advanced analytical techniques to analyze user behavior, content performance, and market trends.
• Drive the development of predictive models and algorithms to personalize user experiences and enhance engagement.
• Define key performance indicators (KPIs) and establish benchmarks to measure the effectiveness of growth and retention initiatives.
• Stay up-to-date with the latest advancements in data science, machine learning, and artificial intelligence, and identify opportunities for their application in the streaming industry.
• Communicate findings and insights to senior management and key stakeholders, and provide recommendations for strategic decision-making.
• Collaborate with engineering teams to deploy data-driven solutions and integrate analytics into products and services.

Qualifications:

• Advanced degree in Computer Science, Statistics, Mathematics, or a related field. 
• Minimum of 4 years of experience in a data science role, with a focus on growth and retention strategies within the entertainment industry, preferably in streaming platforms.
• Strong proficiency in programming languages such as Python, R, or Java, and experience with data analysis and visualization tools (e.g., SQL, Tableau, Matplotlib).
• Proven track record of leading high-performing teams and driving impactful data-driven initiatives in the context of streaming platforms.
• Experience in analyzing user behavior and content performance within streaming platforms is highly desirable, particularly for episodic content.
• Excellent communication and presentation skills, with the ability to translate complex findings into actionable insights for non-technical stakeholders.
• Strong analytical and problem-solving skills, with a keen attention to detail and a passion for continuous learning and improvement.
• Ability to thrive in a fast-paced and dynamic environment, with a proactive and collaborative approach to teamwork.
• Able to work on-site in Sunnyvale, CA for 5 days per week.

Benefits

• Medical, Dental, and Vision benefits

Crazy Maple Studio, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.