Applied Data Scientist - Contract

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.