AI Data Scientist
New
C
CongaRevenue lifecycle management
Canada - RemoteFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 10+ years of professional experience in data science, machine learning, NLP, or AI
- Required Skills
- PythonSQLPyTorchTensorflowNLPLLMGenerative AI
Requirements
- Master’s degree in Computer Science, Data Science, Computational Linguistics, Statistics, Mathematics, Engineering, or a related quantitative field; Ph.D. preferred but not required.
- 10+ years of professional experience in data science, machine learning, NLP, or AI.
- Significant hands-on experience building and deploying NLP and/or GenAI solutions, including taking solutions from experimentation through production and ongoing optimization.
- Expert-level Python and extensive use of Hugging Face Transformers, spaCy, NLTK, Sentence Transformers, PyTorch, and TensorFlow.
- Expertise in traditional NLP, including text classification, named entity recognition, information extraction, semantic similarity, and information retrieval.
- Strong understanding of deep learning architectures and Transformer-based language models.
- 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.
- Strong SQL skills and experience with structured and unstructured data at scale.
- Experience with distributed data processing platforms, cloud platforms, modern ML infrastructure, and ML tooling.
Responsibilities
- Research and evaluate NLP, machine learning, deep learning, and Generative AI techniques, translating useful advances into practical solutions.
- Investigate ambiguous technical problems, form hypotheses, and run experiments comparing approaches.
- Design and build NLP, statistical ML, deep learning, and transformer-based solutions.
- Develop data pipelines, preprocessing, feature representations, embeddings, and training datasets for structured and unstructured data.
- Develop and evaluate LLM and GenAI solutions using prompting, RAG, embeddings, vector search, fine-tuning, agents, and structured generation.
- Design experiments, benchmarks, evaluation datasets, and statistical methods; lead error analysis and performance measurement.
- Deploy NLP and AI solutions to production, monitor them, and optimize their reliability and performance with engineering teams.
- Collaborate with engineering, product, and data science teams, and provide technical guidance and mentorship.
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