Applied Data Scientist - Contract at Arcurve in Calgary, AB, CA

Arcurve is one of North America’s leading full-service technology, advisory and software development companies. In 2006, we began with a belief that there was a better way to deliver professional services in the technology industry. Since then, we have completed more than 1000 projects for clients ranging from start-ups to Fortune 500 companies. From our office in Calgary and hubs in Halifax, Houston, and Vancouver we deliver exceptional results for our clients in a diverse range of industries including telecommunications, oil and gas, transportation, private equity, gaming, infrastructure, software, finance, and hospitality.

At Arcurve, we believe that work should be an enjoyable experience and that the best “aha” moments come through team learning and continuous motivation. We know the key to success is collaboration, and that you can’t put a value on accountable, transparent, and authentic interactions. We strive to deliver exceptional service while creating lasting relationships with our employees, our students, our clients, and our community.

We’re looking for an authentic, collaborative, and accountable Applied Data Scientist to join the Arcurve team.

YOU ARE
• Passionate about technology
• An authentic and creative human
• Driven to succeed
• A believer in the importance of teamwork
• Community-minded
• An expert problem solver
• Someone who thrives on challenge
• Motivated by exceptional results
• Someone who cares about your clients

THE GOAL

To deliver best-in-class technical solutions across a broad array of clients in different industries utilizing the tech stack best suited to solving the problem with a focus on delivering business value for our clients.

THE ROLE
Arcurve delivers applied machine learning for clients operating in complex technical environments. Our project portfolio spans computer vision, timeseries forecasting, causal analysis, and large language model and agentic systems.
As a Data Scientist, you will own the analytical approach on client engagements. You will frame the problem, select or design the method, define how success is measured, and implement a solution of sufficient quality to run in production. You will work alongside a Data Engineer who owns the pipelines and platform, which allows you to concentrate on the modelling itself.
This is an applied role. Theoretical depth matters, and so does the ability to deliver clean, maintainable code that a colleague can extend without assistance.

THE RESPONSIBILITIES
• Design, develop, and validate machine learning models across a range of problem domains and data types.
• Establish evaluation criteria and testing strategy before development begins, and measure performance against them.
• Translate ambiguous business problems into well-defined analytical ones, and select methods that are appropriate to the constraints rather than to fashion.
• Design and evaluate LLM and agentic systems, including the guardrails required for enterprise deployment.
• Define data models and semantic structures that support reliable AI-driven analysis.
• Partner with data engineering to move models into production and monitor their behaviour over time.
• Present findings, methodology, and limitations to technical and business stakeholders.

THE REQUIREMENTS
• Bachelor's degree in Computer Science, Engineering, Statistics, or a related quantitative field, or equivalent practical experience.
• Demonstrated experience as a Data Scientist or Machine Learning Engineer, including models deployed to production and maintained there.
• Expert-level Python and experience with the modern machine learning stack, including scikit-learn, PyTorch, and statsmodels or equivalent libraries.
• Strong SQL.
• Solid grounding in statistics, probability, and linear algebra, sufficient to reason about uncertainty and model assumptions rather than only reporting metrics.
• Working knowledge of Databricks and/or Snowflake, and experience operating in a Spark-backed environment.
• Familiarity with MLOps practice, including experiment tracking, model registry, versioning, monitoring, and retraining.
• Experience delivering in a major cloud environment, with Azure preferred and AWS a strong second.
• Established software engineering habits, including version control, code review, and automated testing.
• Excellent written and verbal communication, including the ability to explain and defend methodology to stakeholders who will challenge it.

THE PERKS
• A fun work atmosphere that values equity, diversity and inclusion.
• Competitive contractor rates.
• Hybrid work environment and flexible scheduling.
• 6-month contract with the possibility of extension.