Data Scientist

6 days ago

Vancouver, Canada Fastloop Full time

The Data Scientist will contribute to the design, development, and implementation of data-driven solutions using a blend of traditional data science, machine learning, and emerging AI techniques, including Generative AI and Large Language Models. Sitting within a cross-functional team, this role bridges analytics and engineering to deliver impactful solutions for client business problems, while continuing to develop technical expertise and AI/ML fluency in a rapidly evolving space.
Key ResponsibilitiesAnalytics & Data Science
- Conduct exploratory data analysis (EDA), statistical testing, and feature engineering to support hypothesis-driven investigations.
- Build, validate, and optimize machine learning models for prediction, classification, clustering, and recommendation tasks.
- Assist in developing structured and semantic data models that support analytics and AI use cases, using Kimball.
- Contribute to the development of dashboards and reports using tools like Looker or Power BI to surface AI-driven insights to stakeholders.
- Work with large, complex datasets using SQL, Python, and other data science tools to uncover actionable business intelligence.

AI/ML Development
- Support training, tuning, and evaluation of supervised and unsupervised ML models using frameworks like scikit-learn, TensorFlow, or PyTorch.
- Contribute to the implementation of data ingestion and transformation pipelines, collaborating with data engineers to ensure scalability and efficiency.
- Participate in model deployment and MLOps practices in collaboration with more senior team members.

Generative AI & Large Language Models (LLMs)
- Explore and implement Generative AI and LLM-based approaches (e.g., Gemini API, LangChain, NotebookLM) in collaboration with the AI/ML team.
- Fine-tune and evaluate foundation models for text summarization, classification, semantic search, Q&A, and conversational interfaces.
- Support the integration of LLMs into client solutions via platforms like AgentSpace and DialogFlow.
- Experiment with prompt engineering, retrieval-augmented generation (RAG), and embedding techniques to enhance LLM performance.
- Stay current on trends in Generative AI, including emerging tooling, capabilities, and ethical implications.

Project Collaboration
- Participate in project scoping discussions and help refine technical requirements under guidance from senior staff.
- Collaborate closely with functional consultants, data engineers, and solution leads to ensure deliverables meet business objectives.
- Clearly communicate results, methodologies, and model implications to both technical and non-technical stakeholders.
- Contribute to documentation and internal knowledge-sharing related to analytics workflows and AI models.

Tools & PlatformsCore Languages & Libraries
- Required Experience
- Python (pandas, numpy, scikit-learn, etc)
- Langchain / huggingface
- SQL
- BigQuery
- Git
- Dbt / Airflow

Google Cloud Platform (GCP) - Preferred Experience
- AI/ML Tools: Vertex AI Workbench, BigQuery ML, Vertex AI AutoML
- Data Engineering: BigQuery, Dataflow, Pub/Sub, Cloud Run Functions, Cloud Storage
- Visualization: Looker, LookML, Looker Studio
- GenAI/LLMs: Gemini API, NotebookLM, LangChain (on GCP), DialogFlow CX
- MLOps & Deployment: Vertex AI Pipelines, Cloud Run, Cloud Logging/Monitoring

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