Junior Data Scientist

Artefact
england, ecr eb
7 months ago
Applications closed

Related Jobs

View all jobs

Junior Data Scientist | London | SaaS Data Platform

Data Science Manager – Gen/AI & ML Projects - Bristol

Staff Data Scientist

Senior Data Engineer (Kafka Expert)

Senior Data Engineer (Kafka Expert)

Software Engineer

Location:London, UK (Hybrid)
Type:Full-Time

Who we are

Artefact is a new generation of data service provider, specialising in data-driven consulting and data-driven digital marketing. We are dedicated to transforming data into business impact across the entire value chain of organisations. With skyrocketing growth, Artefact has established a global presence with over 1,000 employees across 20 offices worldwide.

Our data-driven solutions are designed to meet the specific needs of our clients, leveraging our deep AI expertise and innovative methodologies. Our cohesive teams of data scientists, engineers, and consultants are focused on accelerating digital transformation, ensuring tangible results for every client.

Role Profile

A Data Scientist at Artefact will work together with consultants as a joint team on client projects. Leverage machine learning, AI, and statistical techniques to solve specific business problems.

Responsibilities

Develop and maintain code to deliver data science solutions. Work together with business consultants to understand and document client needs. Follow a structured skill development program aimed at advancing to a Senior Data Scientist role. Contribute to ongoing research and academic initiatives. Simplify and communicate technical concepts to non-technical stakeholders.

Required skills

Data: Design and implement storage solutions with SQL, NoSQL, cloud storage, data versioning, validation, and advanced dataframe handling (Polars/PySpark).Python: Utilise virtual environments, object-oriented programming, data classes, and data manipulation libraries for scripting and visualisation.Repository: Manage code with single-branch PRs/MRs, CI/CD pipelines, pre-commit hooks, and Markdown documentation for building, testing, and deploying.Cloud: Leverage cloud infrastructure (e.g., AWS EC2), databases, and configuration with markup files for remote management and deployment.Model: Implement models (e.g., linear regression, gradient boosting) with training/testing datasets, cross-validation, performance visualisation, and use hosted APIs; explore techniques like time-series forecasting, clustering, or Bayesian inference.Orchestration and Parallelisation: Manage workflows with tools like Metaflow, MLFlow, AirFlow, or DVC; utilise parallelisation frameworks like PySpark or Ray for efficient model processing.

Desirable skills

A Master’s degree in a quantitative field Exposure to cloud platforms (AWS, Azure, GCP)

Why you should join us

Artefact is revolutionizing marketing:join us to build the future of marketingProgress: every day offers new challenges and new opportunities to learnCulture:Check out our website (Artefact.com) or Instagram (Artefact UK) to find out more about our diverse, vibrant culture hereEntrepreneurship: you will be joining a team of driven entrepreneurs. We won’t give up until we make a huge dent in this industry!

Hit apply, and see whether what we offer is what you’ve been looking for!

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Jobs at Newly Funded UK Start-ups: Q3 2025 Investment Tracker

Data science has become an indispensable cornerstone of modern business, driving decisions across finance, healthcare, e-commerce, manufacturing, and beyond. As organisations scramble to capitalise on the insights their data can offer, data scientists and machine learning (ML) experts find themselves in ever-higher demand. In the UK, which has cultivated a robust ecosystem of tech innovation and academic excellence, data-driven start-ups continue to blossom—fuelled by venture capital, government grants, and a vibrant talent pool. In this Q3 2025 Investment Tracker, we delve into the newly funded UK start-ups making waves in data science. Beyond celebrating their funding milestones, we’ll explore the job opportunities these investments have created for aspiring and seasoned data scientists alike. Whether you’re interested in advanced analytics, NLP (Natural Language Processing), computer vision, or MLOps, these start-ups might just offer the career leap you’ve been waiting for.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.