Data Scientist

AirOps

Data Scientist at AirOps shaping AI-driven search environments through advanced machine learning. Designing ML systems to optimize content for AI agents and improve search visibility.

Posted 6/3/2026full-timeNew York City • California, New York • 🇺🇸 United StatesMid-LevelSeniorWebsite

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

machine learningnatural language processingsearch algorithmsrecommendation algorithmsXGBoostrandom foreststransformersgraph neural networksreinforcement learningmodel optimization

Soft Skills

technical leadershipcommunication skillscollaborationinfluencing architecture decisionsimproving team practicesdriving cross-functional projectsexplaining technical conceptsaligning initiatives with business outcomes

Tools & Technologies

model serving frameworksexperiment trackingfeature storesmonitoring systems

Industry Keywords

production machine learning systemsAI search behaviorcontent opportunitiesAI-driven platformsbusiness impact

About the role

Key responsibilities & impact

Design and deploy end-to-end machine learning systems including NLP models, search and recommendation algorithms, and LLM-based applications.
Build ML systems that analyze AI search behavior, identify content opportunities, and predict performance across different AI-driven platforms. Create algorithms that help brands understand and optimize for how AI agents discover and rank content.
Collaborate with product managers to translate business requirements into technical solutions.

Requirements

What you’ll need

5+ years building production machine learning systems with demonstrated business impact; strong background in NLP and search/recommendation systems required
Deep expertise across ML approaches: classical models (XGBoost, random forests), modern deep learning architectures (transformers, graph neural networks), and reinforcement learning systems
Proven ability to take models from research to production, including optimization for latency and cost at scale
Experience with ML infrastructure and tooling: model serving frameworks, experiment tracking, feature stores, and monitoring systems
Track record of technical leadership: influencing architecture decisions, improving team practices, and driving cross-functional projects without direct authority
Excellent communication skills with ability to explain complex technical concepts to non-technical stakeholders and align ML initiatives with business outcomes.

Benefits

Comp & perks

Equity in a fast-growing startup
Competitive benefits package tailored to your location
Flexible time off policy
Parental Leave
A fun-loving and (just a bit) nerdy team that loves to move fast!