Job title:
Machine Learning Engineering Manager
Company
LinkedIn Job Wrapping
Job description
Location: London, UK (Hybrid)
Type: Full-Time
Who we areArtefact 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 1,500 employees across 23 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 OverviewWe are looking for a Machine Learning Manager to support a team of data scientists and ensure the successful delivery of our projects. The ideal candidate will be willing to be hands-on with projects, meaning involvement in model design, coding, and developing end-to-end data solutions, including data preprocessing, visualization, and deploying models into production environments.This role is crucial in
- Driving project success by providing clear direction, solving complex, industry-driven problems, and ensuring high-quality results.
- Leading technical project delivery through hands-on prototyping, design, and coding.
- Leading and upskilling a team of data scientists.
Key responsibilities
- Lead and deliver impactful data transformation projects for clients.
- Build strong client relationships, leveraging your technical expertise to drive operational transformation.
- Participate in international projects with opportunities for business travel.
- Ensure successful project delivery and communicate these successes across the company.
- Foster continuous learning and growth within the data science team.
- Provide mentorship, ensuring high work standards and supporting team well-being.
- Demonstrate technical leadership and contribute to institutional knowledge.
- Embody Artefact’s values and inspire others to do the same.
Qualifications: Education & experience requiredEssential:
- A PhD or Master’s degree in machine learning, mathematics, computer science, or related fields.
- Strong experience in data processing, modelling, algorithms, and architecture.
- Knowledge of CI/CD pipelines and version control systems (e.g., Git).
- Proficiency in building, optimising, deploying, and maintaining machine learning models in a cloud environment.
- Hands-on experience with deep learning frameworks (PyTorch, TensorFlow).
- Experience in initiating and completing projects with minimal guidance.
- Experience in working in one of three industries, automotive, retail or consumer goods.
Desirable:
- Understanding of NLP transformer models and Large Language Models (LLMs), especially in the context of topic classification, sentiment analysis, and advanced language generation tasks.
- Practical experience in building Retrieval-Augmented Generation (RAG) systems for Large Language Models (LLMs), with familiarity in using libraries like LangChain to integrate retrieval and generation workflows effectively.
- Experience with Customer Lifetime Value (CLV) projects, both descriptive and predictive.
- Experience with containerisation and orchestration tools, such as Docker (containerisation) and Metaflow (or equivalent orchestrator, e.g., Airflow, Kubeflow etc.).
- Ability to use distributed computing (e.g., Ray or equivalent).
- Strong understanding of concepts in calculus, linear algebra, probability, and statistics.
What we are looking for:
- A Doer: You get things done and inspire your team to do the same.
- An Analyst: You love data and believe every decision should be driven by it.
- A Pragmatist: You have a hacker mindset and always find quick wins.
- A Mentor: Your clients and teams naturally seek your advice.
- An Adventurer: You’re an entrepreneur constantly looking for problems to solve.
Expected salary
Location
London
Job date
Sat, 12 Oct 2024 22:49:03 GMT
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