AI Data Scientist

Conga•Houston, TX

About The Position

As an AI Data Scientist at Conga, you will be a hands-on technical leader responsible for researching, building, evaluating, and deploying natural language and machine learning capabilities for revenue lifecycle management solutions. You will work across traditional NLP, statistical machine learning, deep learning, and Generative AI, partnering with Engineering, Product, and Data Science teams to take solutions from initial experiments to production at scale. The role focuses on applying NLP and Generative AI to complex commercial documents and decisions, including contracts, quotes, and agreements. You will shape how these capabilities are used across Conga’s platform, influencing product intelligence, model quality, and customer experience. You will also contribute to technical standards and mentor colleagues through applied research and rigorous evaluation practices. The position is listed as Houston—Hybrid or Canada—Remote, reports to the Senior Director, Science | Platform AI - R&D, and involves limited travel to other Conga offices upon request.

Requirements

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 and extensive use of Hugging Face Transformers, spaCy, NLTK, Sentence Transformers, PyTorch, and TensorFlow.
Expert knowledge of traditional NLP techniques, 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.

Nice To Haves

Ph.D. (preferred but not required).
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.

Responsibilities

Research and evaluate emerging NLP, machine learning, deep learning, and Generative AI techniques, and translate promising advances into practical solutions.
Investigate ambiguous technical problems, form hypotheses, and run experiments comparing competing approaches.
Evaluate models, frameworks, and methodologies, adopting those that demonstrate potential to improve product capabilities and system performance.
Design and build traditional NLP, statistical ML, deep learning, and transformer-based solutions using appropriate algorithms, architectures, and representations.
Develop data pipelines, NLP preprocessing, feature representations, embeddings, and training datasets across structured and unstructured data.
Establish data-quality strategies to maintain model reliability as data and use cases evolve.
Develop 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 its platform.
Design experiments, benchmarks, evaluation datasets, and statistical methodologies to measure and improve NLP, ML, and GenAI systems.
Lead error analysis and performance measurement, translating results into clear next steps.
Set evaluation standards for the broader team.
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.
Identify model drift, quality gaps, and optimization opportunities in live systems.
Collaborate 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.