Straive
Lead Data Scientist – News Personalization & Recommendations
Straive, New York, New York, us, 10261
Lead Data Scientist – News Personalization & Recommendations
Location: Orlando, FL (preferred) | Open to NYC or Remote (US)
Team: Data Science & Machine Learning – News
About the Role We are seeking a Lead Data Scientist to drive the strategy and execution of data science initiatives in the news domain, with a particular focus on personalization and recommendation systems. This role is central to shaping how millions of users discover, consume, and engage with news content. The ideal candidate has deep expertise in news recommendation systems, including personalization models that handle fast-changing, short-lived content. They will lead technical initiatives, mentor teams, and collaborate with product, engineering, and editorial partners to build systems that maximize both user engagement and trust.
Responsibilities
Lead the design, development, and optimization of news-focused recommendation systems, including real-time personalization, ranking, and content discovery models.
Build models that account for content freshness, diversity, serendipity, and bias mitigation, which are especially critical in news contexts.
Develop scalable systems leveraging embeddings, contextual bandits, reinforcement learning, graph-based approaches, and hybrid recommendation methods.
Collaborate with engineering to deploy and monitor models in large-scale production environments.
Establish best practices in experimentation (A/B testing, multi-armed bandits, interleaving), evaluation metrics (CTR, dwell time, diversity, novelty), and fairness in recommendations.
Mentor and guide a team of data scientists; establish technical excellence in modeling, analysis, and reproducibility.
Partner with cross-functional stakeholders to define the data science roadmap for news personalization and audience engagement.
Stay ahead of industry/academic advances in recommendation systems, ranking, personalization, and applied ML.
Qualifications
Advanced degree (PhD or MS) in Computer Science, Statistics, Mathematics, or related field.
6+ years of experience in data science or applied machine learning, with at least 2+ years in a lead/principal role.
Strong expertise in recommendation systems, particularly in real-time personalization and session-based recommenders, handling fast-moving, high-churn content like news articles, diversity/novelty trade-offs and mitigating filter bubbles, context-aware and multi-objective optimization for recommendations.
Proficiency in Python, SQL, and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
Hands-on experience with large-scale data infrastructure (Spark, Hadoop, BigQuery, or similar).
Experience with NLP for news content understanding (topic modeling, summarization, entity extraction).
Experience with recommendation libraries and platforms (e.g., TensorRec, LightFM, Implicit, RecBole, Vespa, Nvidia Merlin).
Familiarity with modern embedding techniques (word, document, user embeddings) and vector databases (FAISS, Pinecone, Weaviate).
Excellent communication skills; ability to translate complex ML insights into actionable strategy.
Previous experience in media, news, or content recommendation systems strongly preferred.
Nice-to-Haves
Background in user behavior modeling, cold-start problem solving, or real-time personalization.
Familiarity with modern ML platforms, feature stores, and streaming data systems (e.g., Kafka, Flink).
Benefits
401(k) matching
Medical insurance
Vision insurance
#J-18808-Ljbffr
Team: Data Science & Machine Learning – News
About the Role We are seeking a Lead Data Scientist to drive the strategy and execution of data science initiatives in the news domain, with a particular focus on personalization and recommendation systems. This role is central to shaping how millions of users discover, consume, and engage with news content. The ideal candidate has deep expertise in news recommendation systems, including personalization models that handle fast-changing, short-lived content. They will lead technical initiatives, mentor teams, and collaborate with product, engineering, and editorial partners to build systems that maximize both user engagement and trust.
Responsibilities
Lead the design, development, and optimization of news-focused recommendation systems, including real-time personalization, ranking, and content discovery models.
Build models that account for content freshness, diversity, serendipity, and bias mitigation, which are especially critical in news contexts.
Develop scalable systems leveraging embeddings, contextual bandits, reinforcement learning, graph-based approaches, and hybrid recommendation methods.
Collaborate with engineering to deploy and monitor models in large-scale production environments.
Establish best practices in experimentation (A/B testing, multi-armed bandits, interleaving), evaluation metrics (CTR, dwell time, diversity, novelty), and fairness in recommendations.
Mentor and guide a team of data scientists; establish technical excellence in modeling, analysis, and reproducibility.
Partner with cross-functional stakeholders to define the data science roadmap for news personalization and audience engagement.
Stay ahead of industry/academic advances in recommendation systems, ranking, personalization, and applied ML.
Qualifications
Advanced degree (PhD or MS) in Computer Science, Statistics, Mathematics, or related field.
6+ years of experience in data science or applied machine learning, with at least 2+ years in a lead/principal role.
Strong expertise in recommendation systems, particularly in real-time personalization and session-based recommenders, handling fast-moving, high-churn content like news articles, diversity/novelty trade-offs and mitigating filter bubbles, context-aware and multi-objective optimization for recommendations.
Proficiency in Python, SQL, and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
Hands-on experience with large-scale data infrastructure (Spark, Hadoop, BigQuery, or similar).
Experience with NLP for news content understanding (topic modeling, summarization, entity extraction).
Experience with recommendation libraries and platforms (e.g., TensorRec, LightFM, Implicit, RecBole, Vespa, Nvidia Merlin).
Familiarity with modern embedding techniques (word, document, user embeddings) and vector databases (FAISS, Pinecone, Weaviate).
Excellent communication skills; ability to translate complex ML insights into actionable strategy.
Previous experience in media, news, or content recommendation systems strongly preferred.
Nice-to-Haves
Background in user behavior modeling, cold-start problem solving, or real-time personalization.
Familiarity with modern ML platforms, feature stores, and streaming data systems (e.g., Kafka, Flink).
Benefits
401(k) matching
Medical insurance
Vision insurance
#J-18808-Ljbffr