Staff AI Data Scientist

Description

The company sells commercial operations software that connects the people and processes behind pricing, quoting, contracting, rebates, and buyer communications so teams stay aligned. It serves more than 10,000 customers worldwide, including over 50% of the Fortune 100.

This is a hands-on technical leadership role for an AI Data Scientist building the natural language and machine learning features behind the company's revenue lifecycle products. You will research, build, evaluate, and deploy solutions across traditional NLP, statistical ML, deep learning, and Generative AI, working with Engineering, Product, and Data Science to take work from first experiment to production at scale.

The role suits a scientist who combines research depth with a builder's habits and can weigh theory against practical trade-offs such as data, performance, latency, cost, and business need.

The company's customers use its platform to make sense of complex contracts, quotes, and agreements, which are full of unstructured language. Turning that language into reliable insight is central to the product, and your work will shape how NLP and Generative AI are applied across the platform. You will also raise the team's technical standards through mentoring, evaluation practices, and applied research.

Responsibilities

• Research and test new NLP, machine learning, deep learning, and Generative AI methods, and turn the most promising ones into working solutions.
• Dig into unclear technical problems on your own, form hypotheses, and run experiments that compare competing approaches.
• Keep assessing new models, frameworks, and methods, and adopt those that could improve product capability or system performance.
• Design and build traditional NLP, statistical ML, deep learning, and transformer-based solutions, choosing algorithms, architectures, and representations that fit each problem.
• Build data pipelines, NLP preprocessing, features, embeddings, and training datasets for both structured and unstructured data.
• Set data quality strategies that keep models reliable as the data and use cases change.
• Develop and assess LLM and GenAI solutions using prompting, RAG, embeddings, vector search, fine-tuning, agents, and structured generation.
• Choose and tune models for accuracy, latency, cost, and business requirements.
• Help set how the company builds, grounds, and governs GenAI features across the platform.
• Design experiments, benchmarks, evaluation datasets, and statistical methods to measure and improve NLP, ML, and GenAI systems.
• Lead error analysis and performance measurement, and turn the findings into clear next steps.
• Create evaluation standards that the wider team can adopt and extend.
• Move NLP and AI solutions from prototype through production, deployment, monitoring, and ongoing tuning.
• Work with engineering teams to run models at scale with strong reliability and performance.
• Spot model drift, quality gaps, and optimization opportunities in live systems before they cause problems.
• Work with engineering, product, and data science teams on complex problems and technical direction.
• Mentor colleagues in NLP, ML, experimentation, and GenAI, and encourage curiosity, scientific rigor, and continuous learning.

Requirements

• A master's degree in computer science, data science, computational linguistics, statistics, mathematics, engineering, or a closely related quantitative field.
• Preferred, not required: a Ph.D.
• At least 10 years of professional experience in data science, machine learning, NLP, or AI, including substantial hands-on work building and deploying NLP and/or GenAI solutions.
• A record of taking ML, NLP, or GenAI solutions from experiments through production and ongoing improvement.
• Proven ability to turn open-ended research into practical outcomes you can measure.
• Strong communication skills and a history of working as a senior individual contributor across technical teams.
• Expert Python, with extensive use of Hugging Face Transformers, spaCy, NLTK, Sentence Transformers, PyTorch, and TensorFlow.
• Expert knowledge of traditional NLP: tokenization, stemming, lemmatization, n-grams, TF-IDF, topic modeling, text classification, named entity recognition, information extraction, semantic similarity, and information retrieval.
• Strong understanding of deep learning architectures, including CNNs, RNNs, LSTMs, GRUs, encoder-decoder models, attention, and Transformers.
• Strong understanding of transformer language models: embeddings, pretraining, fine-tuning, and inference.
• Hands-on work with LLMs and foundation models: prompt engineering, RAG, vector search, reranking, fine-tuning, LoRA/QLoRA, structured generation, and LLM evaluation.
• Solid grasp of experimental design, statistical testing, cross-validation, hyperparameter optimization, and error analysis.
• Experience with NLP and GenAI metrics such as precision, recall, F1, ROC-AUC, MAP, MRR, NDCG, semantic similarity, groundedness, and relevance.
• Strong SQL and experience with structured and unstructured data at scale.
• Experience with distributed data platforms such as Spark, Databricks, or BigQuery.
• Experience with cloud platforms such as Databricks, GCP, AWS, or Azure, and modern ML infrastructure.
• Experience with ML tooling such as MLflow, Kubeflow, Weights & Biases, Docker, Kubernetes, or Ray.
• Experience with vector databases and search tools such as FAISS, Elasticsearch/OpenSearch, Pinecone, Weaviate, or Milvus.
• Familiarity with LLM application frameworks such as LangChain, LlamaIndex, or DSPy.
• Gives you an edge: experience with NLP in the legal domain, such as NER, document classification, or semantic search over legal documents.
• Gives you an edge: experience with advanced information retrieval, semantic search, document intelligence, multimodal models, or knowledge graphs.
• Gives you an edge: experience with parameter-efficient fine-tuning, quantization, distillation, inference optimization, synthetic data generation, and agentic architectures.
• Gives you an edge: advanced knowledge of cloud AI/ML platforms and distributed training and inference for large LLM systems.
• Gives you an edge: published papers, patents, open-source work, conference talks, or technical writing.
• Gives you an edge: experience working in Agile, cross-functional product teams.
• Key strengths the company looks for: research depth, a builder's mindset that owns outcomes past the first experiment, scientific rigor in evaluation, clear collaboration and communication, and technical leadership through mentoring and sound judgment.

Conditions

Reports to the Senior Director, Science, Platform AI, R&D.

Limited travel to other company offices, on request.

The company welcomes applicants who do not meet every listed qualification and encourages people from all backgrounds to apply.