Senior Data Scientist

<h2>Who we are</h2>
<ul>
<li><strong>Artefact</strong> is a leading global consulting firm dedicated to accelerating the adoption of data and AI. We work with a variety of businesses, from supermarket chains, to private equity firms and telecoms; including Nissan, L'Oréal, Carrefour, WHSmith, Orange, Beiersdorf, BNP Paribas, and Samsung.</li>
<li>Our success stems from combining advanced data technologies, agile methods for quick delivery, and dedicated teams of data scientists, data engineers, business consultants, and data analysts.</li>
<li>Our <strong>1,800 employees</strong> operate in <strong>25 countries</strong> (Americas, Europe, Asia, Middle East, India, Africa) and we partner with <strong>1,000+ clients</strong>.</li>
</ul>
<h2>What you will be doing</h2>
<p>As a <strong>Senior Data Scientist </strong>in our <strong>London office</strong>, your role will encompass:</p>
<ul>
<li>Designing and implementing advanced data science and machine learning solutions to solve complex business problems.</li>
<li>Taking ownership of project streams, from defining technical deliverables and timelines to presenting updates to client steering committees.</li>
<li>Supervising and mentoring team members on code, deployment, and best practices.</li>
<li>Architecting and deploying robust, scalable solutions using modern cloud technologies and MLOps principles.</li>
</ul>
<h2>Qualifications</h2>
<h3>Necessary education and experience</h3>
<ul>
<li><strong>Education</strong>: A Bachelor's or Master’s degree in Computer Science, Mathematics, Statistics, Physics, Engineering, or a related quantitative field.</li>
<li><strong>Project &amp; Team Leadership</strong>: Demonstrable experience supervising team members, taking responsibility for project delivery, defining technical tasks, and presenting project updates to both internal and client stakeholders.</li>
<li><strong>Advanced Modelling</strong>: Proven ability to implement a range of complex models such as time-series forecasting, gradient boosting, clustering, NLP, and Bayesian inference.</li>
<li><strong>ML-Ops &amp; Orchestration</strong>: Strong experience with MLOps tools for orchestration, experiment tracking, hyper-parameter tuning, and deploying automated model retraining pipelines.</li>
<li><strong>Programming &amp; Data Engineering</strong>: Proficiency in object-oriented Python, advanced dataframes (Polars/Pyspark), and data versioning (DVC). Experience designing data storage solutions and using object-oriented SQL interfaces.</li>
<li><strong>Cloud &amp; DevOps</strong>: Hands-on experience with at least two major cloud providers (AWS, Azure, GCP), including app deployment, database services (e.g., RDS, CosmosDB), and infrastructure-as-code (Terraform). Solid understanding of CI/CD for testing and containerisation.</li>
</ul>
<h3>Desirable experience</h3>
<ul>
<li><strong>Advanced Education</strong>: A Master's degree or PhD in a relevant field is a strong plus.</li>
<li><strong>Parallelisation &amp; Performance</strong>: Experience with parallelisation frameworks like Pyspark or Ray.</li>
<li><strong>Advanced Cloud &amp; Infrastructure</strong>: Familiarity with serverless deployments (e.g., Fargate, Lambdas), infrastructure automation with Terratest or Ansible.</li>
</ul>
<p> </p>

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