Staff Data Scientist

Job Title:

Staff Data Scientist (AI \& Machine Learning)

Location:

Vancouver, BC – 3 days a week onsite

Salary Range:

$180-$200k CAD

Overview:

As a Staff Data Scientist, you are a technical visionary and a strategic leader within our AI organization. While a Senior Data Scientist excels at building and deploying specific models, you are responsible for the scientific roadmap and architectural patterns that define how we apply AI across our global communications archive.

You will lead high-impact, cross-functional initiatives in machine translation, transcription, and Generative AI, ensuring that our models are not only accurate but also scalable and highly available in a big data environment. You will bridge the gap between cutting-edge research and enterprise-grade production, serving as the primary technical authority for our most complex ML challenges.

What You’ll Do:

• Scientific Leadership:

Lead the technical strategy for NLP and LLM initiatives. You will define the methodologies for how we classify and understand communication content at an astronomical scale.

• Architecture \& Scalability:

Partner with Engineering and DevOps to design robust ML platforms. You will oversee the transition from experimental notebooks to high-availability microservices within Kubernetes environments.

• End-to-End Governance:

Own the entire data science lifecycle at a systemic level—from defining data collection strategies to optimizing inference performance using tools like TensorRT.

• Strategic Problem Solving:

Identify and tackle "cross-cutting" technical issues that affect multiple pods. You will pre-emptively solve bottlenecks in data engineering and model latency before they impact the business.

• Culture \& Mentorship:

Act as a force multiplier for the Vancouver data science community. You will mentor Senior Data Scientists, lead design reviews, and instill a rigorous engineering culture (code quality, reproducibility, and TDD) within the data science discipline.

• Business Synthesis:

Collaborate with Product Leadership to transform ambiguous business problems into achievable AI/ML roadmaps, ensuring our AI features deliver clear ROI for our customers.

Tech Stack \& Domain Expertise

• Core Languages:

Python (Expert), Bash.

• Frameworks:

PyTorch, TensorFlow, Scikit-learn, TensorRT.

• Specializations:

LLMs, Generative AI, Natural Language Processing (NLP), Speech Recognition (ASR).

• Environment:

Kubernetes, Microservices, Big Data Ecosystems (Hadoop/Spark), Agile.

What You Must Have:

• 10\+ years

of experience in software or data science, with at least

8\+ years

specifically solving complex machine learning problems at scale.

• Academic Foundation:

BSc in a STEM or Linguistics subject (Advanced degrees highly preferred).

• Enterprise Big Data Experience:

Proven track record of deploying models into production environments that handle massive, sensitive datasets.

• Leadership Tenure:

Extensive experience mentoring junior, intermediate, and senior data scientists, with a history of improving team-wide processes and code standards.

Advanced Technical Skills:

• Deep Learning Mastery:

Expert-level knowledge of neural network architectures, particularly Transformers and their application in NLP and Generative AI.

• Performance Engineering:

Ability to optimize model inference for low-latency, high-throughput production requirements (e.g., quantization, pruning, and hardware acceleration).

• Data Engineering Fluency:

Strong ability to design data pipelines and cleaning processes that ensure high-quality training sets from unstructured communication archives.

• Operational Excellence:

Deep understanding of MLOps, CI/CD for ML, and monitoring model drift in a production setting.

The "Staff" Edge (Preferred):

• Advanced Degree:

MSc or PhD in a STEM or Linguistics field.

• Systemic Impact:

Experience building shared ML libraries, feature stores, or internal platforms used by other data science teams.

• Thought Leadership:

Contributions to the wider AI community through publications, patents, or open-source contributions.