At Fastloop, we have a deep passion for helping businesses build their future. We use leading technologies and best-in class business expertise within our three core pillars of service excellence: Data, Analytics and AI (including Generative AI and Machine Learning).

As a boutique consultancy, our client engagements cover a broad spectrum of the modern data stack, leveraging platforms like Google Cloud to quickly and iteratively produce Analytics and AI outcomes for our clients.

Fastloop's consulting model requires a team who is willing and able to work quickly, across new industries/verticals, with emerging technologies, in a fast, agile fashion. All team members must be extremely proactive, willing to explore the unknown and comfortable adapting to changing technical and functional requirements.

Fastloopers are technology and consulting experts who want to understand how our customers companies' operate from the inside out. We are passionate about adding significant operational value, treating our clients' businesses as our own. We are problem solvers who like to roll up our sleeves and accelerate digital transformation journeys - always leveraging data as our foundation.

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.

• 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.

• 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.
• Apply version control and reproducibility practices to maintain quality and traceability of data science assets.
• Participate in model deployment and MLOps practices in collaboration with more senior team members.

• 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.

• 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.

• Python (pandas, numpy, scikit-learn, etc)
• Langchain / huggingface
• SQL
• BigQuery
• Git
• Dbt / Airflow

• 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

Please note: This is not a remote opportunity. Only candidates who reside in the Greater Vancouver area will be considered.