Data Scientist, Personalization & Machine Learning at OpenAI

Job description

About the Team We work across product, growth, and monetization to help people get more value from OpenAI’s products. We identify opportunities for machine learning, build and test early solutions, and partner with engineering and product to bring successful approaches into production. About the Role We’re looking for a data scientist who can enter an unfamiliar product area, find a useful ML application, and turn it into a working product intervention. You will own the work from problem definition and data preparation through modeling, experimentation, and launch evaluation. Projects will include personalization, next-best-action recommendations, lifecycle management, promotion targeting, and models of retention and customer lifetime value. You will help identify opportunities, build proofs of concept, evaluate, and iterate. In this role, you will: - Identify opportunities with product and business partners, define the decision a model will inform, and prioritize work by user value, feasibility, and expected impact. - Build datasets, features, reproducible training workflows, and models. You’ll use a combination of tools appropriate to the task at hand. - Translate predictions into product actions. Design experiments and use causal reasoning to establish whether those actions improve user and business outcomes. - Partner with engineers on integration, serving or batch scoring, monitoring, and rollout. Stay involved through launch and iteration. - Explain results and limitations clearly, document decisions, and build reusable methods that other teams can adopt. You might thrive in this role if you: - Have built and evaluated applied ML systems that influenced a product or business decision, with clear ownership of the modeling work. - Write effective Python and SQL and can investigate imperfect data, debug models, and make pragmatic implementation choices. - Build and schedule production scoring pipelines using dbt, airflow, and flyte. - Understand supervised learning, evaluation, experimentation, and the difference between predicting an outcome and changing it. - Can scope ambiguous problems, work across disciplines, and explain technical tradeoffs to colleagues with different backgrounds. - Are motivated by helping people use AI effectively and by learning unfamiliar product domains. - Are excited to take on challenging, impactful ML problems across multiple areas. - Have experience with recommendation systems, personalization, growth, retention, lifetime value, or treatment-effect modeling would be useful. We welcome candidates whose relevant experience comes from other domains. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement cdn.openai.com. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form form.asana.com. No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link form.asana.com. OpenAI Global Applicant Privacy Policy cdn.openai.com At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

Requirements

Must-haves

About the Team
Identify opportunities with product and business partners, define the decision a model will inform, and prioritize work by user value, feasibility, and expected impact.
Build datasets, features, reproducible training workflows, and models. You’ll use a combination of tools appropriate to the task at hand.
Translate predictions into product actions. Design experiments and use causal reasoning to establish whether those actions improve user and business outcomes.
Partner with engineers on integration, serving or batch scoring, monitoring, and rollout. Stay involved through launch and iteration.
Explain results and limitations clearly, document decisions, and build reusable methods that other teams can adopt.
You might thrive in this role if you:
Have built and evaluated applied ML systems that influenced a product or business decision, with clear ownership of the modeling work.
Write effective Python and SQL and can investigate imperfect data, debug models, and make pragmatic implementation choices.
Build and schedule production scoring pipelines using dbt, airflow, and flyte.
Understand supervised learning, evaluation, experimentation, and the difference between predicting an outcome and changing it.
Can scope ambiguous problems, work across disciplines, and explain technical tradeoffs to colleagues with different backgrounds.
Are motivated by helping people use AI effectively and by learning unfamiliar product domains.
Are excited to take on challenging, impactful ML problems across multiple areas.
Have experience with recommendation systems, personalization, growth, retention, lifetime value, or treatment-effect modeling would be useful. We welcome candidates whose relevant experience comes from other domains.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlpythonsqlai

Platform: ashby
Effort: quick