About the Team:
Our Technology team drives the evolution of our Technology, Engineering, Data, Product and User Experience functions. With a keen focus on delivering cutting-edge solutions, we shape the digital landscape for our customers, readers and users. From revolutionizing visuals to optimizing tools and harnessing the power of data, mobile, video and social platforms, our team is committed to providing a seamless and immersive experience across all touchpoints. Collaborating closely with our newsrooms and strategic partners, we spearhead the development of groundbreaking products and technologies.
About the Role:
Dow Jones is seeking a Lead Data Scientist to own and advance the architecture and delivery of AI-powered Search and discovery platforms across our products. As part of the GenAI, Search, and Personalization organization, this role will specifically lead Search data science while collaborating closely with peer leads across Generative AI and Personalization.
You will define and execute the technical strategy for scalable, production-grade search systems, building and optimizing machine learning and information retrieval pipelines that power relevance, ranking, and content discovery. This role emphasizes applied, engineering-focused data science, partnering closely with engineering teams to design reliable, high-performance search infrastructure and deploy modern capabilities such as semantic retrieval, hybrid and vector search, retrieval-augmented generation (RAG), and LLM-powered conversational discovery experiences. This is a hands-on leadership role focused on delivering robust, scalable AI search systems that drive measurable business impact.
You Will:
- Own the end-to-end delivery of search and discovery data science initiatives, from problem framing and experimentation through production deployment and performance monitoring.
- Establish and evolve relevance and evaluation frameworks, defining success metrics and driving continuous improvement in search quality, engagement, and business impact.
- Design and scale retrieval and ranking pipelines that operate reliably in production and support evolving product and user needs.
- Partner closely with engineering to ensure search capabilities are built as scalable, maintainable platform components rather than one-off models or experiments.
- Collaborate with product and editorial stakeholders to translate user and business requirements into effective discovery experiences.
- Work alongside peer leads across Generative AI and Personalization to deliver cohesive and consistent user discovery strategies.
- Provide technical leadership and mentorship to data scientists, setting high standards for experimentation, model development, and production readiness.
- Identify and drive new opportunities to enhance search capabilities, proactively shaping the long-term roadmap for AI-driven discovery.
You Have:
- Master’s or Ph.D. in Computer Science, Information Retrieval, Machine Learning, Data Science, or a related quantitative field (or equivalent practical experience).
- 4 - 7 years of industry experience in applied machine learning, search, or information retrieval, including experience leading complex, cross-functional initiatives.
- Strong expertise in modern search and discovery systems, including ranking, relevance optimization, query and user intent understanding, and large-scale retrieval architectures.
- Hands-on experience building and deploying semantic, vector, and hybrid search solutions, including familiarity with embedding models and retrieval-augmented generation (RAG) patterns.
- Deep experience applying machine learning and/or LLM-based techniques to real-world discovery, recommendation, or knowledge retrieval problems.
- Advanced programming skills in Python and strong experience with ML and LLM ecosystems (e.g., PyTorch, Hugging Face, retrieval and evaluation tooling, or similar frameworks).
- Experience designing experimentation and evaluation frameworks for search quality, including offline relevance metrics and online testing methodologies.
- Proven experience deploying production ML systems using cloud infrastructure (e.g., AWS, GCP, or similar), including performance optimization and scalability considerations.
- Familiarity with distributed systems, data pipelines, and production deployment practices, including containerization and orchestration technologies.
- Strong collaboration and communication skills, with experience translating user and business needs into scalable technical solutions.
- Demonstrated leadership and mentorship experience, with a track record of elevating technical standards and supporting team growth.
Our Benefits
- Comprehensive Insurance Plans
- Paid Time Off
- Family Care Benefits
- Access to Dow Jones Products
- Subscription Discounts
- Employee Referral Program
- Employee Well-being Support & Fitness Programs