Staff AI Data Scientist

AI Data ScientistAt Conga, we've built a community where our colleagues can thrive. Here you'll find opportunities to innovate and support growth through individual and team development, all within an environment where every voice is heard.Conga lines up commercial operations so companies run as connected, smarter businesses. By unifying the people and processes that drive commerce, Conga aligns pricing, quoting, contracting, rebates, and communications so teams stay in sync and buyers keep moving forward. The result is trusted decisions, consistent buyer experiences, and profitable growth. More than 10,000 customers worldwide, including over 50% of the Fortune 100®, trust Conga when commercial complexity is high and global impact is on the line.As an AI Data Scientist at Conga, you will be a hands-on technical leader responsible for researching, building, evaluating, and deploying the natural language and machine learning capabilities that power our revenue lifecycle management solutions. You will work across traditional NLP, statistical machine learning, deep learning, and Generative AI to solve complex product and business problems, partnering closely with Engineering, Product, and Data Science teams to take solutions from first experiment to production at scale.This role is ideal for a scientist who pairs deep research instincts with a builder's mindset. You understand both the theory and the practical tradeoffs behind NLP and ML, and you know how to choose the right approach based on data, performance, latency, cost, and business need.At Conga, AI is central to how we help customers make sense of their most complex commercial documents and decisions. Contracts, quotes, and agreements are rich with unstructured language, and turning that language into trusted insight is one of the biggest opportunities in front of us.As an AI Data Scientist, you will help shape how Conga applies NLP and Generative AI across our platform. Your work will directly influence product intelligence, model quality, and the experience thousands of customers rely on every day. You'll also help raise the technical bar across the team through mentorship, rigorous evaluation standards, and a steady stream of applied research.You'll work alongside a highly talented team of scientists and engineers dedicated to solving hard problems while supporting products that help customers streamline and transform their revenue operations.Applied Research & InnovationResearch and evaluate emerging NLP, machine learning, deep learning, and Generative AI techniques, translating the most promising advances into practical solutions.Investigate ambiguous technical problems independently, form hypotheses, and run experiments that compare competing approaches.Continuously evaluate new models, frameworks, and methodologies, adopting those with demonstrated potential to improve product capabilities and system performance.NLP & Machine Learning DevelopmentDesign and build traditional NLP, statistical ML, deep learning, and transformer-based solutions using the right algorithms, architectures, and representations for the problem.Develop data pipelines, NLP preprocessing, feature representations, embeddings, and training datasets across structured and unstructured data.Establish data-quality strategies that keep models reliable as data and use cases evolve.Generative AI & LLM SolutionsDevelop and evaluate LLM and GenAI solutions using prompting, RAG, embeddings, vector search, fine-tuning, agents, and structured generation.Select and optimize models based on accuracy, latency, cost, and business requirements.Help define how Conga builds, grounds, and governs GenAI capabilities across the platform.Evaluation & Experimentation ExcellenceDesign rigorous experiments, benchmarks, evaluation datasets, and statistical methodologies to measure and improve NLP, ML, and GenAI systems.Lead error analysis and performance measurement that turn results into clear next steps.Set evaluation standards the broader team can adopt and build on.Production & Operational ExcellenceTake NLP and AI solutions from research and prototype through production deployment, monitoring, and ongoing optimization.Partner with engineering teams to operationalize models at scale with strong reliability and performance.Proactively identify model drift, quality gaps, and optimization opportunities in live systems.Cross-Functional Partnership & MentorshipCollaborate with engineering, product, and data science teams to solve complex problems and shape technical direction.Provide technical guidance and mentorship in NLP, ML, experimentation, and GenAI.Foster a culture of curiosity, scientific rigor, and continuous learning.Are you the person we're looking for?Master's degree in Computer Science, Data Science, Computational Linguistics, Statistics, Mathematics, Engineering, or a related quantitative field. Ph.D. preferred but not required.10+ years of professional experience in data science, machine learning, NLP, or AI, including significant hands-on experience building and deploying NLP and/or GenAI solutions.Proven experience taking ML, NLP, or GenAI solutions from experimentation through production deployment and ongoing optimization.Demonstrated ability to translate open-ended research into practical, measurable outcomes.Excellent communication skills and a track record of working effectively as a senior individual contributor across technical teams.Technical ExpertiseExpert-level Python with extensive use of Hugging Face Transformers, spaCy, NLTK, Sentence Transformers, PyTorch, and TensorFlow.Expert knowledge of traditional NLP, including 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 mechanisms, and Transformers.Strong understanding of Transformer-based language models, including embeddings, pretraining, fine-tuning, and inference.Hands-on experience with LLMs and foundation models, including prompt engineering, RAG, vector search, reranking, fine-tuning, LoRA/QLoRA, structured generation, and LLM evaluation.Strong grasp of experimental design, statistical testing, cross-validation, hyperparameter optimization, and error analysis.Experience with NLP and GenAI evaluation metrics, including precision, recall, F1, ROC-AUC, MAP, MRR, NDCG, semantic similarity, groundedness, and relevance.Strong SQL skills and experience with structured and unstructured data at scale.Experience with distributed data processing 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 technologies such as FAISS, Elasticsearch/OpenSearch, Pinecone, Weaviate, or Milvus.Familiarity with LLM application frameworks such as LangChain, LlamaIndex, or DSPy.Key CompetenciesResearch DepthYou bring strong theoretical foundations and the curiosity to explore new techniques, while staying grounded in what will actually move the product forward.Builder's MindsetYou enjoy getting hands-on, turning prototypes into production systems, and owning outcomes well past the first experiment.Scientific RigorYou hold yourself and your team to high standards for evaluation, measurement, and evidence-based decisions.Collaboration & CommunicationYou partner effectively across functions and explain complex technical concepts clearly to diverse audiences.Technical LeadershipYou elevate those around you through mentorship, thoughtful guidance, and sound technical judgment.Here's what will give you an edge…Experience with NLP in the legal domain, such as NER, document classification, or semantic search over legal documents.Experience with advanced information retrieval, semantic search, document intelligence, multimodal models, or knowledge graphs.Experience with parameter-efficient fine-tuning, model quantization, distillation, inference optimization, synthetic data generation, and agentic architectures.Advanced knowledge of cloud AI/ML platforms and distributed training and inference for large-scale LLM systems.Contributions to the field through publications, patents, open-source projects, conference talks