Principal Data Scientist - AI

Anaplan•New York, NY

7h•$257,000 - $348,000•Hybrid

About The Position

At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market. Our customers rank among the who’s who in the Fortune 50. Coca-Cola, LinkedIn, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best-in-class platform. Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebrating our wins – big and small. Supported by operating principles of being strategy-led, values-based and disciplined in execution, you’ll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let’s build what’s next - together! We're seeking a versatile Principal Data Scientist who can work across the full stack of Anaplan AI applications, from model integration and prompt engineering to building intuitive user interfaces. In this role, you will have a direct impact on our industry-leading platform by architecting and deploying cutting-edge AI solutions that drive intelligent, automated, and forward-looking decision-making for our global customers. You will bridge the gap between traditional predictive algorithms and modern generative systems.

Requirements

10+ years of hands-on professional experience in the field of Artificial Intelligence, Machine Learning, or related engineering domains.
End-to-end exposure in model lifecycle development, including extensive experience in training and deploying ML models in production environments.
Deep knowledge of LLM APIs, prompt engineering, and conversational AI patterns.
Experience in fine-tuning LLMs for domain-specific enterprise applications.
Proficiency in Python and modern software development practices (testing, code review, CI/CD).
Proven track record of delivering complex technical projects on time with high quality.
Extensive hands-on experience with traditional Machine Learning, Deep Learning techniques, and Timeseries Forecasting algorithms.
Deep technical understanding of Transformer architectures and practical experience fine-tuning LLMs (e.g., LoRA, QLoRA, RLHF)
Strong expertise in MLOps and LLMOps, ensuring scalable, reliable, and monitorable model deployments.
Experience with agentic frameworks and autonomous agent architectures.
Proficiency in Python and modern software development practices (testing, code review, CI/CD).

Nice To Haves

M.S. or Ph.D. in Computer Science, Artificial Intelligence, Statistics, Data Science, or a related quantitative field.
Published research, patents, or significant contributions to open-source projects in the AI/ML community.
Experience working with scalable cloud infrastructure (AWS, GCP, or Azure) and MLOps tools for model training, monitoring, and deployment.
Background in forecasting, planning, or analytics applications
Familiarity with Anaplan or similar enterprise planning platforms
Experience with A/B testing and experimentation frameworks for AI features
Contributions to open-source ML projects or research publications
Experience with model observability tools

Responsibilities

Lead the research, design, and implementation of advanced Machine Learning (ML) and Deep Learning (DL) models to solve complex enterprise business problems.
Drive innovation in Timeseries Forecasting, developing robust and scalable models that enhance predictive planning and scenario modeling capabilities.
Architect Generative AI solutions, focusing on fine-tuning open-source and proprietary Large Language Models (LLMs) for specialized enterprise data tasks, heavily leveraging Transformer architectures.
Design and integrate Conversational AI interfaces and Agentic AI workflows to create intuitive, autonomous user experiences that can independently execute complex planning workflows.
Collaborate with cross-functional teams (Engineering, Product, and Design) to transition AI models from prototype to robust, scalable production systems.
Serve as a subject matter expert, mentoring other data scientists and engineers, and fostering a culture of technical excellence, rigorous testing, and continuous learning.