Machine Learning Engineer
Posted 2 days ago by Sanderson
£750 Per day
Undetermined
Remote
London Area, United Kingdom
Summary: The Machine Learning Engineer role involves joining a FTSE 100 retail client's data science function to implement advanced machine learning technologies. The position requires expertise in timeseries forecasting and a strong background in data science and machine learning. The role is fully remote and offers a competitive daily rate. The initial contract duration is six months.
Key Responsibilities:
- Drive cutting-edge machine learning technology across the business.
- Implement timeseries forecasting solutions.
- Utilize SQL and Python for data analysis.
- Develop machine learning services using GCP and VertexAI.
- Apply knowledge of machine learning algorithms and deep learning techniques.
Key Skills:
- Significant experience as a Data Scientist/Machine Learning Engineer.
- Solid knowledge of SQL and Python’s ecosystem (Jupyter, Pandas, Scikit Learn, Matplotlib).
- Experience with GCP and VertexAI.
- Strong understanding of computer science fundamentals.
- Knowledge of machine learning algorithms and modern deep learning techniques.
Salary (Rate): £750 daily
City: London Area
Country: United Kingdom
Working Arrangements: remote
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Machine Learning Engineer £700-750/day overall assignment rate to umbrella Fully remote 6 month initial A FTSE 100 retail client are on the look for a Machine Learning Engineer to join their data science function to drive cutting-edge ML technology across the business. MUST have previous experience of Timeseries forecasting. Machine Learning Engineer, key skills: Significant experience working as a Data Scientist/Machine Learning Engineer Solid knowledge of SQL and Python’s ecosystem for data analysis (Jupyter, Pandas, Scikit Learn, Matplotlib). GCP, VertexAI experience is desirable (developing GCP machine learning services) Timeseries forecasting Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling and software architecture Strong knowledge of Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) as well as state-of-the-art research area (e.g. NLP, Transfer Learning etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.)