AI Data Scientist

Role Overview

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.

Responsibilities

• Researching, building, evaluating, and deploying natural language and machine learning capabilities
• Working across traditional NLP, statistical machine learning, deep learning, and Generative AI to solve complex product and business problems
• Working closely with Engineering, Product, and Data Science teams to take solutions from first experiment to production at scale

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 with extensive use of Hugging Face Transformers, spaCy, NLTK, Sentence Transformers, PyTorch, and TensorFlow
• Strong understanding 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, 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 Competencies

• Research Depth: Bringing strong theoretical foundations and the curiosity to explore new techniques, while staying grounded in what will actually move the product forward.
• Builder’s Mindset: Enjoying getting hands-on, turning prototypes into production systems, and owning outcomes well past the first experiment.
• Scientific Rigor: Holding yourself and your team to high standards for evaluation, measurement, and evidence-based decisions.
• Collaboration & Communication: Partnering effectively across functions and explaining complex technical concepts clearly to diverse audiences.
• Technical Leadership: Elevating those around you through mentorship, thoughtful guidance, and sound technical judgment.