Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global visual discovery platform helps millions of people around the world discover creative ideas, explore new possibilities, and plan meaningful experiences. The organization is focused on inspiring people to create lives they love while fostering a culture of innovation, collaboration, flexibility, and continuous growth.
The organization also views Artificial Intelligence as a powerful partner that enhances creativity and amplifies impact. Its teams use AI to improve products, strengthen decision-making, and create meaningful experiences for people, creators, advertisers, and merchants worldwide.
The organization is seeking a Staff Data Scientist, Ads Delivery to shape the future of people-facing and business-facing products. This role will apply quantitative modeling, experimentation, machine learning, and advanced algorithms to solve complex engineering and business challenges.
The successful candidate will collaborate with Product, Engineering, Design, Research, Product Analytics, Data Engineering, and other cross-functional teams. The work will introduce greater scientific rigor into product development and influence products serving hundreds of millions of users, creators, advertisers, and merchants around the world.
Key Responsibilities
- Develop a deep and nuanced understanding of ads delivery systems while quantifying opportunities and risks across the full customer funnel.
- Lead projects focused on ads delivery opportunities across different stages of the funnel.
- Design and productionize robust, scalable machine learning and evaluation frameworks covering forecasting, recommendation, and causal inference.
- Advocate for best-in-class experimentation, instrumentation, and metric design.
- Bridge the gap between short-term proxy metrics and long-term business impact.
- Collaborate with Product, Engineering, Research, Business, and Design teams to translate complex data questions into actionable business insights.
- Mentor and guide junior and senior data scientists while fostering intellectual curiosity and technical excellence.
- Influence product development teams by applying rigorous scientific methodologies and data-driven decision-making.
- Identify and address complex product, engagement, and ecosystem analytics challenges.
- Drive cross-functional initiatives and align diverse stakeholders toward shared business and product objectives.
Required Qualifications
- 10+ years of hands-on experience working in web-scale data environments.
- Proven track record of solving complex and ambiguous problems in product, engagement, or ecosystem analytics.
- Deep expertise in machine learning, including recommendation, ranking, prediction, and experimentation.
- Strong knowledge of statistical modeling and causal inference using both observational and experimental data.
- Strong product analytics and strategy capabilities beyond dashboard development, including root-cause analysis, goal setting, and design collaboration.
- Advanced programming skills in Python or R.
- Advanced SQL and Spark experience.
- Strong product intuition with the ability to scope, question, and design effective solutions for ambiguous, high-impact business problems.
- Strong scientific rigor and healthy skepticism, with the ability to challenge assumptions, identify flaws, and drive toward robust and reproducible outcomes.
- Exceptional written and verbal communication skills, with the ability to simplify complex concepts for both technical and non-technical audiences.
- Demonstrated experience mentoring and developing data science talent at the Staff or Senior Individual Contributor level.
- Strong cross-functional leadership skills and the ability to align competing interests toward shared objectives.
- Bachelor’s or Master’s degree in Computer Science or another relevant field, or equivalent professional experience.
Preferred Expertise
- Experience working with large-scale advertising delivery systems.
- Expertise in recommendation and ranking systems.
- Experience designing and evaluating experimentation frameworks.
- Strong understanding of causal inference methodologies.
- Experience translating quantitative analysis into product and business strategies.
- Experience partnering closely with engineering, product, research, design, and business teams.
- Ability to leverage AI effectively as part of data science and problem-solving workflows.
Disclaimer
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