AI Data Scientist

United StatesRemoteFull-timemid

Data ScientistData

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Quick Summary

Overview

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for an AI Data Scientist based in United States .

Technical Tools

Data ScientistData

We are seeking an experienced AI Data Scientist to research, develop, and deploy advanced natural language processing (NLP), machine learning, and generative AI solutions for complex business applications. In this role, you will transform unstructured documents and large-scale datasets into reliable insights that support smarter commercial decisions. You will combine deep scientific expertise with hands-on engineering to take innovative ideas from early experimentation to production-ready systems. Working closely with engineering, product, and data science teams, you will shape AI capabilities, improve model performance, and establish rigorous evaluation standards. This is an opportunity to influence the direction of AI-powered products while mentoring colleagues and contributing to a collaborative, research-driven environment focused on practical innovation and measurable impact.

Research and evaluate emerging techniques in NLP, statistical machine learning, deep learning, and generative AI, translating promising advances into practical product capabilities.
Independently investigate complex technical challenges, develop hypotheses, conduct experiments, and compare alternative models, architectures, and methodologies.
Design and implement NLP and machine learning solutions using appropriate algorithms, representations, and transformer-based architectures.
Develop data pipelines, preprocessing workflows, feature representations, embeddings, and training datasets for structured and unstructured information.
Establish data quality practices that maintain model reliability as datasets, applications, and business requirements evolve.
Build generative AI and large language model solutions using prompt engineering, retrieval-augmented generation (RAG), embeddings, vector search, fine-tuning, AI agents, and structured generation.
Optimize model selection and performance by balancing accuracy, latency, computational cost, and business requirements.
Design rigorous experiments, benchmarks, evaluation datasets, and statistical methodologies to measure model quality and identify opportunities for improvement.
Conduct detailed error analysis and apply relevant evaluation metrics to guide model refinement and support evidence-based decisions.
Move AI solutions from research and prototyping into production, collaborating with engineering teams to ensure scalability, reliability, monitoring, and ongoing optimization.
Monitor deployed systems for model drift, quality issues, and performance bottlenecks, implementing improvements as needed.
Partner with product, engineering, and data science stakeholders to shape technical direction and align AI capabilities with business objectives.
Provide technical leadership and mentorship in NLP, machine learning, generative AI, and experimental design while promoting scientific rigor and continuous learning.

Requirements

~2 min read

A master’s degree in computer science, data science, computational linguistics, statistics, mathematics, engineering, or a related quantitative discipline. A PhD is preferred but not required.
At least 10 years of professional experience in data science, machine learning, NLP, or AI, including substantial hands-on experience developing and deploying NLP or generative AI solutions.
Demonstrated success taking machine learning, NLP, or generative AI projects from experimentation through production deployment and continuous optimization.
Strong ability to translate open-ended research questions into practical solutions with measurable business and product outcomes.
Expert-level Python proficiency and extensive experience with libraries and frameworks such as Hugging Face Transformers, spaCy, NLTK, Sentence Transformers, PyTorch, and TensorFlow.
Deep 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.
Practical knowledge of transformer-based language models, including embeddings, pretraining, fine-tuning, and inference.
Hands-on experience with large language models and foundation models, including prompt engineering, RAG, vector search, reranking, fine-tuning, LoRA/QLoRA, structured generation, and LLM evaluation.
Strong understanding of experimental design, statistical testing, cross-validation, hyperparameter optimization, and error analysis.
Experience with NLP and generative AI evaluation metrics, including precision, recall, F1, ROC-AUC, MAP, MRR, NDCG, semantic similarity, groundedness, and relevance.
Strong SQL skills and experience working with structured and unstructured data at scale, including distributed processing platforms such as Spark, Databricks, or BigQuery.
Familiarity with cloud platforms such as GCP, AWS, or Azure and modern machine learning infrastructure, including tools 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, and familiarity with frameworks such as LangChain, LlamaIndex, or DSPy.
Excellent communication and collaboration skills, with the ability to explain complex technical concepts clearly and provide effective technical guidance across teams.
Preferred qualifications: Experience applying NLP to legal documents, document classification, semantic search, document intelligence, multimodal models, or knowledge graphs; expertise in model quantization, distillation, inference optimization, synthetic data generation, agentic architectures, and distributed LLM training or inference.
Additional advantages include publications, patents, open-source contributions, conference presentations, technical writing, and experience working in Agile, cross-functional product teams.