Applied Data Scientist - Contract

Royal Credit Union Inc.; Calgary; Hybrid; CAD 96,000 - 165,000; Part time

Benefits offered by this job

Hybrid work environment

Flexible scheduling

Competitive contractor rates

6-month contract with extension

Job summary

Arcurve seeks an authentic, collaborative Applied Data Scientist to deliver best-in-class machine learning solutions across diverse industries. You will design, validate, and productionize models, including LLM and agentic systems, while translating complex problems into actionable analytics for clients and internal stakeholders.

You will collaborate with data engineering to ensure reliable production monitoring and maintainability, prioritizing business value and clean code for long-term impact.

Qualifications

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

Responsibilities

• Design, develop, and validate machine learning models across diverse domains and data types.
• Establish evaluation criteria and testing strategy before development and measure performance.
• Translate ambiguous business problems into well-defined analytical tasks and select appropriate methods.
• Design and evaluate LLM and agentic systems, including guardrails for enterprise deployment.
• Define data models and semantic structures to support reliable AI-driven analysis.
• Collaborate with data engineering to productionize models and monitor their behavior.
• Present findings, methodology, and limitations to technical and business stakeholders.

Skills

Python

ML Engineering

SQL

Databricks

Snowflake

MLOps

Azure

Git

Communication

Education

Bachelor's degree in CS/Eng/Stats or related

Tools

scikit-learn

PyTorch

statsmodels

Databricks

Snowflake

Spark

Job description

We’re looking for an authentic, collaborative, and accountableApplied Data Scientistto 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.

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.