Staff Data Scientist

Clio is the global leader in legal AI technology, empowering legal professionals and law firms of every size to work smarter, faster, and more securely. Responsibilities Architectural Strategy & Forecasting Leadership Define the long‑term forecasting roadmap and establish architectural standards for the entire company. Lead the architectural design of complex ML systems, ensuring they are scalable, maintainable, and integrated seamlessly with the broader engineering stack.

Architect reusable, "platform‑level" forecasting frameworks to be used by other Data Science teams. High‑Impact Modeling Own the development of "tier‑1" models—those with the highest business risk or technical complexity—using advanced statistical, deep learning, or reinforcement learning techniques. Tackle high‑ambiguity challenges, such as integrating causal inference or deep learning into global forecasts.

GenAI & LLM Innovation Act as the subject‑matter expert for Generative AI; design RAG architectures, evaluate foundation models, and establish fine‑tuning protocols for proprietary data. MLOps & Quality Assurance Define the team’s technical standards for CI/CD, model versioning, and automated testing. Audit codebases to ensure high‑performance and "production‑ready" quality.

Strategic Experimentation Own the end‑to‑end design and evaluation of high‑impact product and business experiments, establishing rigorous statistical standards and frameworks to ensure trustworthy, data‑driven decisions. Technical Mentorship & Influence Provide deep technical guidance to more junior Data Scientists. Influence the roadmap by identifying "blind spots" in current data strategy and proposing novel solutions.

Cross‑Functional Translation & Ethics Partner with Data Engineering to optimize data pipelines for ML and with Product to ensure technical feasibility of the long‑term vision. Lead the implementation of Model Interpretability (XAI) frameworks to ensure all automated decisions are transparent and unbiased. Qualifications & Skills 8+ years of experience in Data Science, Machine Learning, or a highly quantitative field, with a track record of deploying models that drove significant revenue or cost savings.

Mastery of System Design and state‑of‑the‑art time‑series research. Leadership without Authority: Proven ability to influence executive leadership, mentor senior technical staff, and lead large‑scale technical projects across multiple teams without being a direct people manager. Deep Technical Stack: Expertise in Python and SQL.

Advanced experience with AWS and Databricks. Proficiency in distributed computing (Spark, Ray) and modern data platforms (Snowflake/Databricks). Modern AI Toolkit: Hands‑on experience with LLM orchestration (LangChain, Haystack), vector databases, and PyTorch or TensorFlow.

System Design: Strong understanding of software engineering principles (microservices, API design) as they relate to deploying ML at scale. Education: Master’s or Ph.D. in Computer Science, Physics, Statistics, Mathematics, or a related quantitative field. Communication: Ability to persuade executive leadership on technical investments and "sell" a long‑term technical vision.

Compensation and Benefits The expected salary range for this role is $153,600 to $230,400 CAD. Salary bands are region‑specific and can vary based on experience and skill set. Benefits include: Competitive, equitable salary with top‑tier health benefits, dental, and vision insurance.

Hybrid work environment with a minimum of twice‑per‑week office presence for local Clions (Vancouver, Calgary, Toronto, Dublin, and Sydney). Flexible time‑off policy with an encouraged 20 days off per year. $2,000 annual counseling benefit. RRSP matching and RESP contribution.

Clioversary recognition program with special acknowledgement at 3, 5, 7, and 10 years. EEO & Diversity Statement We are committed to equal employment and encourage candidates from all backgrounds to apply. We pride ourselves on building and fostering an environment where our teams feel included, valued, and enabled to do their best work, wherever they choose to log in from. #J-18808-Ljbffr