AI Data Scientist

Accountabilities

• 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:

• 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.

Benefits:

• Remote work opportunity for candidates based in Canada.

• Opportunities to contribute to innovative AI and machine learning capabilities with direct product and customer impact.

• A collaborative environment that supports professional growth, individual development, and continuous learning.

• Opportunities to work alongside experienced scientists, engineers, and product teams on complex technical challenges.

• The ability to influence AI strategy, improve technical standards, and mentor other team members.

• Limited travel to other company offices when requested.