Remote Data Jobs

A passionate and skilled AI/ML Engineer to develop, implement, and optimize machine learning models and AI-driven solutions. A key player in the team, contributing to designing state-of-the-art systems that drive automation and decision-making.

Serves as a technical expert and strategic partner responsible for designing, developing, and maintaining advanced data architectures and processing systems that support the National Park Service’s mission. This role oversees the full data lifecycle from requirements capture and database design to the creation and optimization of complex data pipelines. The position involves working with large and diverse datasets.

Play a critical part in Tyndale's mission to harness data for advanced AI and Machine Learning applications. Collaborate with data scientists, and business stakeholders to build and maintain robust data pipelines, ensuring data architecture supports the full lifecycle of AI initiatives. This is an exciting opportunity to shape the data foundation of AI and Machine Learning efforts, driving impactful insights and innovations.

Design and execute advanced pricing models, lead experimentation efforts, and uncover insights that power monetization and growth. Work cross-functionally with Product, Analytics, Revenue, and Engineering to ensure our pricing strategies are data-driven, rigorously tested, and aligned with customer value. This is a high-impact opportunity for someone who thrives at the intersection of data science, economics, and strategy.

The Journey GIS Data Specialist plays a vital role in supporting the National Park Service’s mission by developing, maintaining, and optimizing advanced geospatial data systems. This position leverages expertise in relational database management, geospatial data modeling, and enterprise geodatabase operations to ensure high-quality, secure, and accessible data. Requires strong technical skills, a commitment to data integrity, and the ability to collaborate across multidisciplinary teams.

Transform customer lifecycle data into actionable insights that drive marketing success. You will own the lifecycle and marketing analytics domain, building dashboards, analyzing campaigns, and measuring program effectiveness. The ideal candidate is technically skilled and eager to deepen their marketing analytics expertise, comfortable writing efficient SQL queries and presenting clear insights to stakeholders.

Research, prototype, and productionize generative AI models. Develop scalable GenAI pipelines that generate high-quality content, from product descriptions, reviews, titles, and other product content. Design and evaluate prompt tuning strategies and RAG systems to ensure factual and engaging outputs. Fine-tune foundation models and develop domain-specific adapters using techniques like LoRA, PEFT, and instruction tuning.

Expanding the data modeling layer, building trustworthy foundations for analytics, and enabling teams across the company—from Product to GTM to Finance—with scalable, reliable data. This role will report directly to the Head of Data and you’ll partner with them and our Staff Data Engineer to build a reliable data foundation upon which Honeycomb can scale our business.

Work on embedding latent representations of entities across our platform, specifically embedding entities to understand user behavior across multi-touch fragmented journeys in a continuous cold start setting. Design, code, experiment and implement models and algorithms to enhance customer satisfaction, increase supplier value, optimize business results, and ensure infrastructure efficiency.

Analytics Engineers sit at the intersection of business teams, Data Analytics and Data Engineering and are responsible for bringing robust, efficient, and integrated data models and products to life. Analytics Engineers speak the language of business teams and technical teams, they are able to translate data insights and analysis needs into models powered by the Enterprise Data Platform. The successful Analytics Engineer is able to blend business acumen with technical expertise and transition between business strategy and data development.