Associate Data Scientist

Keypath Education

2d•Remote

About The Position

Keypath Education is a leading EdTech company that partners with universities to design, launch, and manage high-quality online degree programs. We combine data-driven decision-making with deep sector expertise to help our university partners grow enrolment, improve student outcomes, and expand access to education. The Opportunity We are looking for an Associate Data Scientist to join our Strategy & Insights team. This is a foundational role designed for someone at the start of their data science career who is eager to learn, build, and grow. Reporting to our Data Scientist, you will be embedded in real commercial projects from day one—contributing to predictive models, generative AI solutions, and analytical work that directly shapes how Keypath operates. We are open to two types of candidates for this role: Career transitioners: You have a background in analytics, research, engineering, or a quantitative discipline and have completed (or are completing) a Master’s degree to formalise your transition into data science. You bring structured thinking, analytical maturity, and professional experience alongside your postgraduate training. High-potential graduates: You are a recent or upcoming Master’s graduate with a strong academic foundation in data science, computer science, statistics, or a related quantitative field. You may be light on professional experience, but you have the technical ability, intellectual curiosity, and ambition to develop into a future leader in the field.

Requirements

Bachelor's or Master’s degree (completed or near-completion) in data science, statistics, computer science, mathematics, engineering, economics, or a related quantitative field.
Proficiency in Python for data analysis and modelling (pandas, NumPy, scikit-learn, or equivalent). We are looking for evidence you can write functional code, not perfection.
A working understanding of core statistical and machine learning concepts: regression, classification, model evaluation, overfitting, and train/test methodology.
Strong analytical thinking and problem-solving ability—the capacity to break an ambiguous question into a structured approach.
Clear written and verbal communication skills - you should be able to explain your reasoning and findings to someone without a technical background.
Genuine curiosity about data science, AI, and how models drive real-world decisions. We want to see that you actively engage with the field—whether through projects, study, writing, or community involvement.

Nice To Haves

Exposure to large language models (LLMs), prompt engineering, or generative AI tools and concepts.
Familiarity with SQL for data extraction and manipulation.
Experience with version control (Git) and collaborative development practices.
Familiarity with cloud platforms, particularly Microsoft Azure or Microsoft Fabric.
A strong capstone, thesis, or applied research project demonstrating end-to-end data science work (Kaggle competitions, GitHub repositories, and published research also count).
Previous professional experience in analytics, research, consulting, or a quantitative role (for career transitioners).

Responsibilities

Support the development of predictive models across the student lifecycle, including lead scoring, conversion propensity, retention risk, and demand forecasting.
Assist in building and evaluating generative AI solutions, such as LLM-powered agents, prompt engineering workflows, and retrieval-augmented generation (RAG) pipelines.
Perform exploratory data analysis, feature engineering, and data wrangling to prepare datasets for modelling.
Write clean, well-documented Python code for analysis and model development, contributing to shared codebases and notebooks.
Help productionise models by supporting pipeline development, testing, and integration with downstream systems.
Prepare visualisations, summaries, and presentations that communicate analytical findings and model outputs to non-technical stakeholders.
Contribute to the team’s knowledge base by documenting methods, sharing learnings, and participating in code reviews.
Proactively develop your own skills through structured learning, certifications, and hands-on experimentation.