Principal Full-Stack Data Scientist - Foundational Models

Royper Royco LLC; Northern (KY); Hybrid; USD 200,000 - 237,000; Full time

Benefits offered by this job

Equity compensation

Annual bonus

Healthcare (medical, dental, vision)

Job summary

Royper Royco LLC seeks a Principal Full-Stack Data Scientist focused on Foundational Models. You will lead ML systems for personalization, embeddings, retrieval, and recommendations at scale.

Own end-to-end lifecycle from prototyping to production, with emphasis on rigorous experimentation and measurable business impact. You will collaborate with cross-functional teams to translate research into reusable capabilities, mentoring colleagues and shaping technical direction in a remote role across

Qualifications

• Bachelor’s degree in a quantitative field; Master’s/PhD preferred.
• 8+ years designing and deploying ML solutions, esp. personalization/recommendations.
• Strong Python programming and production-grade code development.
• Proven ability to lead ambiguous ML problems end-to-end.
• Experience with large-scale datasets using SQL and distributed processing.
• Hands-on with PyTorch or TensorFlow; strong evaluation and experimentation.

Responsibilities

• Design, evaluate, and deploy production-ready ML models for personalization.
• Advance modeling capabilities: embeddings, retrieval, ranking, recommendations.
• Explore LLMs, deep learning, multimodal modeling and generative AI.
• Own end-to-end ML lifecycle from problem to production monitoring.
• Conduct offline and online experiments to measure business impact.
• Collaborate with product, engineering, and data science teams.
• Mentor Data Scientists and engineers; contribute to technical standards.

Skills

Leadership

Communication

Mentoring

Problem-solving

Adaptability

Education

Bachelor’s degree in CS/Statistics/Math/Physics

Master’s or PhD preferred

Tools

PyTorch

TensorFlow

Python

SQL

Apache Spark

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Principal Full-Stack Data Scientist – Foundational Models based in the United States.

Join a collaborative, innovation-driven team building foundational machine learning systems that power personalized experiences and intelligent recommendations at scale.
In this role, you will shape the future of personalization by developing advanced models, embeddings, retrieval systems, and recommendation technologies.
You will own the complete machine learning lifecycle, from identifying opportunities and prototyping new approaches to deploying, monitoring, and optimizing production models.
Working with large-scale behavioral, transactional, textual, and visual datasets, you will explore cutting-edge techniques in deep learning, generative AI, and multimodal modeling.
Your work will directly influence product experiences and business outcomes through rigorous experimentation, measurable improvements, and scalable ML solutions.
Partnering with Data Scientists, ML Engineers, Product teams, and other technical stakeholders, you will translate research into reliable, reusable capabilities.
This remote opportunity is ideal for a seasoned technical leader who combines strong hands-on engineering skills with scientific curiosity, strategic thinking, and a passion for impactful innovation.

Accountabilities:

• Design, develop, evaluate, and deploy production-ready machine learning models that improve personalization, recommendations, and search-related experiences.
• Advance foundational modeling capabilities, including client and item representations, embeddings, retrieval and ranking systems, recommendation algorithms, and assortment generation.
• Explore and implement modern machine learning approaches, including large language models (LLMs), deep learning, representation learning, multimodal modeling, and generative AI.
• Own the end-to-end machine learning lifecycle, from problem definition, data exploration, and prototyping to experimentation, production deployment, monitoring, and continuous improvement.
• Develop rigorous offline evaluations and online experiments, including A/B tests, to assess model performance and quantify client and business impact.
• Analyze and process large-scale behavioral and product datasets using Python, SQL, and distributed computing technologies.
• Build reliable, scalable, and production-quality ML systems while balancing model quality, latency, computational cost, observability, and operational resilience.
• Collaborate with Product, Engineering, Data Science, and other technical teams to understand downstream requirements and deliver reusable foundational models and capabilities.
• Contribute to the team's technical direction through architecture and design discussions, research, prototyping, code reviews, and the development of modeling and engineering best practices.
• Communicate complex modeling approaches, technical trade-offs, research findings, and recommendations clearly to technical and non-technical stakeholders.
• Mentor fellow Data Scientists and engineers, share expertise, and help elevate technical standards across the organization.

Requirements

• Bachelor's degree in Computer Science, Statistics, Mathematics, Physics, or another quantitative discipline; a Master's degree or PhD is preferred.
• At least 8 years of experience designing, developing, and deploying machine learning solutions, ideally in personalization, recommendation systems, search, or representation learning.
• Strong Python programming skills, with the ability to architect technical solutions and develop maintainable, production-grade code.
• Demonstrated ability to independently lead ambiguous machine learning problems from initial exploration and prototyping through deployment, monitoring, iteration, and measurable business impact.
• Experience working with large-scale datasets using SQL and distributed data-processing technologies such as Apache Spark.
• Hands-on experience with modern deep learning frameworks, including PyTorch or TensorFlow.
• Strong understanding of model evaluation and experimentation, including offline metrics, A/B testing, statistical assessment, and the translation of model improvements into measurable product outcomes.
• Ability to assess and manage production ML trade-offs involving predictive quality, scalability, latency, reliability, infrastructure requirements, and computational costs.
• Experience developing or applying modern machine learning techniques, such as deep learning, embeddings, representation learning, recommendation algorithms, LLMs, or multimodal models.
• Strong technical leadership and problem-solving skills, with the ability to establish architecture, influence technical decisions, and drive projects forward in complex or ambiguous environments.
• Excellent communication and collaboration skills, including the ability to explain complex technical concepts to diverse audiences and build alignment across cross-functional teams.
• Intellectual curiosity, adaptability, and a demonstrated commitment to continuous learning and applying emerging research to practical challenges.

Benefits

• Annual salary: $200,000–$237,000 USD, with compensation determined by experience, professional background, location, and other relevant factors.
• Equity compensation: Eligibility for new-hire and ongoing restricted stock unit (RSU) grants, subject to applicable performance and company conditions.
• Annual bonus: Eligibility for an annual bonus.
• Comprehensive healthcare: Medical, dental, and vision insurance.
• Additional benefits: Access to other employee benefits and health and wellness programs.
• Remote flexibility: Opportunity to work remotely within the United States.
• Collaborative culture: Work alongside talented, multidisciplinary professionals in an environment that values innovation, integrity, trust, and diverse perspectives.
• Professional growth: Opportunities to influence technical strategy, explore emerging AI and machine learning techniques, and contribute to high-impact projects.
• Ownership and impact: A culture that encourages autonomy, creative problem-solving, continuous learning, and accountability.
• Values-driven environment: A commitment to inclusion, equity, sustainability, teamwork, and meaningful client experiences.