Lead Data Scientist - Artificial Intelligence R&D
Role details
Job location
Tech stack
Job description
The AI Research & Development (AI R&D) team at Cat Digital is seeking a Lead Data Scientist to serve as a senior technical leader at the intersection of applied AI, advanced technology, and forward-looking research. This role focuses on designing, building, evaluating, and scaling AI prototypes and Proofs of Concept (POCs) with clear production intent, while also contributing to longer-horizon research initiatives.
The Lead Data Scientist will work hands-on across the full AI lifecycle, from data preparation and experimentation to model evaluation and production-readiness, collaborating closely with Product, Engineering, and Business stakeholders to translate emerging AI capabilities into impactful enterprise solutions.
What You Will Do:
- Stay current with emerging AI research by conducting ongoing literature reviews across core AI workstreams such as speech/voice, vision, multimodal systems, retrieval, and autonomous agents, and actively apply relevant innovations to Cat Digital's AI R&D portfolio.
- Assess and compare academic and industry advancements to guide architecture choices, experimentation strategy, and production-readiness decisions.
- Synthesize research findings into practical, enterprise-relevant insights that influence prototyping priorities and long-term AI strategy.
- Lead hands-on experimentation and development of advanced machine learning and generative AI solutions, including LLMs, multimodal models (text, vision, speech), retrieval-augmented generation (RAG), agents, and simulation/digital-twin use cases.
- Design, build, and curate high-quality datasets for training, fine-tuning, validation, and evaluation of AI models at scale.
- Define and execute rigorous model evaluation strategies, including benchmarking model quality, accuracy, latency, cost, robustness, and safety tradeoffs.
- Drive rapid prototyping and POC development with a strong focus on reproducibility, experiment tracking, and observability to enable informed technical decision-making.
- Research, compare, and optimize model architectures, algorithms, and AI system designs to improve performance, scalability, and enterprise readiness.
- Partner with Product and Engineering teams to translate research outcomes and prototypes into production-ready capabilities, including defining technical requirements and success metrics.
- Communicate complex technical findings and insights clearly to both technical and non-technical stakeholders, influencing roadmap and investment decisions.
Requirements
- Structured AI Technology Research: Demonstrated experience conducting structured technology research and literature reviews in advanced AI domains, with the ability to translate theoretical innovation into applied, enterprise-grade solutions.
- Applied Statistics & Quantitative Methods: Extensive experience applying statistical thinking to experimentation, evaluation, and decision-making in ambiguous, research-driven environments.
- Analytical Rigor & Attention to Detail: Proven ability to design precise experiments, validate assumptions, and ensure accuracy and reproducibility of results.
- Advanced Machine Learning & AI: Extensive knowledge of modern ML techniques, including deep learning, generative AI, NLP, computer vision, and multimodal systems, with hands-on implementation experience.
- Model Evaluation & Optimization: Strong experience evaluating model quality and system-level tradeoffs across accuracy, latency, cost, and scalability dimensions.
- Programming Expertise: Extensive proficiency in Python for AI and ML development, including use of modern AI frameworks and tooling.
- Data Engineering & Access: Strong understanding of data storage, retrieval, and processing systems required to support large-scale training and experimentation workflows.
- Requirements & Systems Thinking: Ability to define technical and non-functional requirements that bridge research, engineering, and production concerns.
Considerations for Top Candidates:
- Master's, or PhD degree in Data Science, Computer Science, Machine Learning, Statistics, Applied Mathematics, Engineering, or a closely related technical field.
- Extensive experience building and deploying advanced ML models beyond traditional analytics use cases.
- Extensive proficiency in Python (NumPy, Pandas, PyTorch, LangChain, etc.); ability to write clean, maintainable, production-oriented code and contribute to shared AI infrastructure.
- Strong hands-on experience with generative AI, large language models, deep neural networks, and modern ML frameworks.
- Demonstrated experience designing evaluation frameworks and benchmarks for AI systems.
- Familiarity with AI infrastructure, cloud platforms (AWS, Azure), and scalable experimentation environments.
- Advanced experience with version control, experiment tracking, and collaborative development (e.g., Git-based workflows).
- Experience working in Agile, cross-functional product development environments.
- Prior exposure to industrial, manufacturing, heavy equipment, or complex physical systems is a strong plus, but not required.
Benefits & conditions
Subject to plan eligibility, terms, and guidelines. This is a summary list of benefits.
- Medical, dental, and vision benefits*
- Paid time off plan (Vacation, Holidays, Volunteer, etc.)*
- 401(k) savings plans*
- Health Savings Account (HSA)*
- Flexible Spending Accounts (FSAs)*
- Health Lifestyle Programs*
- Employee Assistance Program*
- Voluntary Benefits and Employee Discounts*
- Career Development*
- Incentive bonus*
- Disability benefits
- Life Insurance
- Parental leave
- Adoption benefits
- Tuition Reimbursement