AI Data Scientist

Conga•Houston, TX

2d•Hybrid

About The Position

Conga is hiring an AI Data Scientist to research, build, evaluate, and deploy natural language and machine learning capabilities for its revenue lifecycle management solutions. The role spans traditional NLP, statistical machine learning, deep learning, and Generative AI, taking solutions from experimentation through production at scale. The scientist will select approaches based on data, performance, latency, cost, and business needs, and contribute to product intelligence, model quality, and customer experience across Conga’s platform. The position includes applied research and experimentation, development of NLP and ML solutions and data pipelines, implementation and evaluation of LLM and GenAI capabilities, and operationalization and monitoring of models in production. The AI Data Scientist will partner with Engineering, Product, and Data Science teams, help shape technical direction, establish evaluation standards, and mentor colleagues. The role is hybrid in Houston or remote in Canada, with limited travel to other Conga offices upon request. It reports to the Senior Director, Science | Platform AI - R&D.

Requirements

Master's degree in Computer Science, Data Science, Computational Linguistics, Statistics, Mathematics, Engineering, or a related quantitative field.
At least 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.
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.
Research depth, curiosity, and the ability to balance theoretical exploration with product impact.
A hands-on builder's mindset and willingness to own outcomes from prototype through production.
Scientific rigor and high standards for evaluation, measurement, and evidence-based decisions.
Ability to collaborate across functions and clearly explain complex technical concepts to diverse audiences.
Technical leadership demonstrated through mentorship, thoughtful guidance, and sound technical judgment.

Nice To Haves

Ph.D. in a relevant quantitative field.
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 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, translating promising advances into practical solutions.
Independently investigate ambiguous technical problems, form hypotheses, and run experiments comparing approaches.
Evaluate new 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 using structured and unstructured data.
Establish data-quality strategies that 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, and help define how Conga builds, grounds, and governs GenAI capabilities.
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 next steps and establishing 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, and identify model drift, quality gaps, and optimization opportunities.
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, fostering curiosity, scientific rigor, and continuous learning.