Machine Learning Engineer (Time Series) at Deep.Meta [40,000 - 70,000
- Employer
- Deep.Meta
- Location
- London
- Salary
- 40000.00 - 70000.00 GBP Annual
- Closing date
- 13 Sep 2022
View more
- Employer Sector
- Technology, ICT & Telecoms
- Contract Type
- Permanent
- Hours
- Full Time
- Travel
- None
- Job Type
- Machine Learning
Salary 40,000 - 70,000 GBP per year
Requirements:
-
Confident developing machine learning models using complex time-series data
Experience using Machine Learning for Anomaly detection
Track record in developing and deploying end-to-end data science pipelines in an industrial or business context to time and to budget.
Fluent with typical Machine Learning stack and tools: Python, Pandas, SciPy, scikit-learn, and one or more deep learning frameworks: TensorFlow, PyTorch etc
Experience using modern software development tools (version control, CI/CD, containers, etc.) in a cloud platform (GCP, AWS)
Responsibilities:
-
Be a part of the development of your team members and facilitate growth in their roles
Ownership and continuous development of Machine Learning algorithms to improve production efficiency in the Metals Industry
Oversee the compatibility of machine learning models with software and improve in-line performance.
Developing timelines for delivering projects to customers and communicating what these are as well as any changes to the Team
Provide technical oversight and leadership on algorithm development
Consult on company decisions and provide informed input
Technologies:
- Python
- AWS
- Cloud
- PyTorch
- TensorFlow
- Data-Science
- Machine Learning
More:
We re an industry 4.0 Startup Backed by Google and Imperial College Enterprise Labs. Our products help fight climate change by reducing production waste and energy consumption in the metals industry, which collectively contribute more than 10% to global greenhouse gas emissions.At Deep.Meta we strongly believe metals industries are as crucial in today s society as they were a century ago. The majority have remained technologically conservative and have masses of underutilized data they could be using to optimize their production and supply chain.
We re optimisation enthusiasts! This is why we are simplifying the most complex problems in the metals industry, using insights from deep learning to develop Machine Learning software that is directly deployable to production lines.
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