Senior Data Scientist, NLP and Publisher Content

About

is the world's leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates,  empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products—
Performance (affiliate),
Creator (influencer), and Advocate (customer referral)—unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust,  helps brands show up where it matters most. Today, over 5,000 global brands, including Walmart, Uber, Shopify, Lenovo, L Oréal, and Fanatics, rely on  to power more than 225,000 partnerships that deliver measurable business results.

This role is hands-on and end-to-end: you'll own modeling and experimentation work from problem framing through productionization in partnership with Business Stakeholder, Engineering, Product, and MLOps. A key medium-term focus will be improving and upscaling our proof of concept for product detection in creator posts (image + text), enabling richer downstream experiences across search, targeting, and recommendations.

• Build NLP models to classify, summarize, and extract structured signals from publisher pages and creator posts (topics, entities, intent, brand/product mentions, sentiment where relevant).
• Develop robust content embeddings and semantic similarity systems for retrieval, clustering, and taxonomy/category modeling.
• Create scalable evaluation frameworks for content models (gold sets, weak supervision, human-in-the-loop labeling, error analysis).

• Turn content signals into targeting features that improve advertiser/publisher matching, relevance, and performance.
• Define and maintain feature sets that are reliable at scale (coverage, latency, cost), resilient to drift, and aligned with privacy and platform constraints.
• Collaborate with Product to translate targeting goals into measurable model outcomes (lift, precision/recall, coverage, monetization impact).

• Apply recommender and ranking techniques to improve discovery and relevance across content and product surfaces.
• Build and tune ranking signals for page ranking at scale, incorporating quality, relevance, and engagement proxies where appropriate.
• Contribute to multimodal search pipelines (text + image) using embeddings and candidate generation/reranking approaches.

• Improve and scale our PoC for product detection in creator posts: enhance accuracy, expand category coverage, reduce operational friction, and harden the pipeline for production use.
• Partner with Engineering to product ionize image-based product signals (detection/classification), integrate them with text signals, and make outputs usable for downstream search/targeting/ranking systems.
• Lead systematic iteration loops: dataset improvements, labeling strategies, model retraining, threshold tuning, and failure-mode analysis.

• Design offline metrics and online experiments (A/B tests, holdouts, interleaving where relevant) to quantify impact and guide tradeoffs.
• Build monitoring for model quality and system health: drift detection, coverage, performance regressions, and alerting.
• Communicate results clearly and drive decisions through crisp narratives and dashboards.

• Own end-to-end ML…