AI Data Scientist

Overview

Hands-on technical leader who researches, builds, evaluates and deploys the natural language and machine learning capabilities that power Conga's revenue lifecycle management solutions, taking solutions from first experiment to production at scale.

What you'll do

• 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.
• 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.
• Establish data-quality strategies that keep models reliable 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 the platform.
• Design 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.
• 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.
• Proactively 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.

What you'll need

• 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 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 have

• 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.

Details

• Location: Houston (hybrid) or Canada (remote).
• Reports to the Senior Director, Science | Platform AI - R&D.
• Limited travel to other Conga offices upon request.