AI Data Scientist

Remote · US - Houston, TX · Texas · 10+ years experience · Posted Oct 16, 2026

At a glance

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.

Research 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.
Design 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.
Develop and evaluate LLM and GenAI solutions using prompting, RAG, embeddings, vector search, fine-tuning, agents, and structured generation.
Design rigorous experiments, benchmarks, evaluation datasets, and statistical methodologies to measure and improve NLP, ML, and GenAI systems.
Take 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.
Collaborate with engineering, product, and data science teams to solve complex problems and shape technical direction while providing mentorship.

Master's degree in Computer Science, Data Science, Computational Linguistics, Statistics, Mathematics, Engineering, or a related quantitative field.
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.
Expert-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 grasp of experimental design, statistical testing, cross-validation, hyperparameter optimization, and error analysis.
Strong SQL skills and experience with structured and unstructured data at scale.

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, or technical writing.
Experience working within Agile, cross-functional product development teams.

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