176 to 200 of 404 scikit-learn Jobs in London

ability to lead end-to-end AI projects—from data exploration and model development to deployment and evaluation. Expert-level proficiency in Python and ML libraries such as Scikit-learn, XGBoost, TensorFlow, PyTorch, and/or Hugging Face. Experience working with large-scale datasets, cloud platforms (AWS/GCP), and ML Ops tools is a plus. Excellent communication More ❯

ability to lead end-to-end AI projects—from data exploration and model development to deployment and evaluation. Expert-level proficiency in Python and ML libraries such as Scikit-learn, XGBoost, TensorFlow, PyTorch, and/or Hugging Face. Experience working with large-scale datasets, cloud platforms (AWS/GCP), and ML Ops tools is a plus. Excellent communication More ❯

cycle processes and tools: CI/CD, version control (git), testing frameworks, MLOps Comfortable working with Docker and containerised applications Experience with data science Python libraries such as Scikit-learn, Pandas, NumPy, Pytorch etc. Experience using AWS or similar cloud computing platform Great communicator - convey complex ideas and solutions in clear, precise and accessible ways Team player who More ❯

background will include: 3+ years industry experience in a Data Science role and a strong academic background Python Data Science Stack: Advanced proficiency in Python , including pandas , NumPy , scikit-learn , and Jupyter Notebooks . Statistical & ML Modelling: Strong foundation in statistical analysis and proven experience applying a range of machine learning techniques to solve business problems (e.g., regression More ❯

background will include: 3+ years industry experience in a Data Science role and a strong academic background Python Data Science Stack: Advanced proficiency in Python , including pandas , NumPy , scikit-learn , and Jupyter Notebooks . Statistical & ML Modelling: Strong foundation in statistical analysis and proven experience applying a range of machine learning techniques to solve business problems (e.g., regression More ❯

machine learning to real world commercial problems Experience bringing live services using machine learning and python to production. Expert knowledge of Python and relevant libraries (numpy, pandas, matplotlib, Scikit-learn, tensorflow, etc...) knowledge in other programming languages is valuable, but this is primarily a Python shop. Experience with things like CI/CD pipelines, Docker or similar, cloud More ❯

background will include: 3+ years industry experience in a Data Science role and a strong academic background Python Data Science Stack: Advanced proficiency in Python , including pandas , NumPy , scikit-learn , and Jupyter Notebooks . Statistical & ML Modelling: Strong foundation in statistical analysis and proven experience applying a range of machine learning techniques to solve business problems (e.g., regression More ❯

of successfully delivering complex projects. Technical Skills Proficiency in programming languages such as Python, R, Java, or C++. Experience with data science tools and frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Knowledge of industrial automation systems and protocols (e.g., SCADA, PLCs, DCS). Familiarity with database management and data visualization tools (e.g., SQL, Tableau, Power BI). Soft More ❯

developments in the ML/AI ecosystem and bring fresh ideas to the table What we’re looking for: Strong Python skills and experience in ML libraries like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience More ❯

developments in the ML/AI ecosystem and bring fresh ideas to the table What we’re looking for: Strong Python skills and experience in ML libraries like scikit-learn, PyTorch, TensorFlow , or XGBoost Hands-on experience building and deploying ML models into production environments Familiarity with ML Ops workflows (e.g., MLflow, Airflow, Weights & Biases, or Kubeflow) Experience More ❯

processing tools, e.g. by using Hadoop/Spark/SQL Experience with or ability to quickly learn open-source software including machine learning packages, such as Pandas, scikit-learn, along with data visualisation technologies. Experience in retail sector would be an added advantage What you can expect from us We won't just meet your expectations. We More ❯

tuning LLMs for NLP tasks in industry. Demonstrable industry experience delivering AI/ML frameworks for a product. Expertise in working with ML frameworks such as PyTorch, Tensorflow, scikit-learn, Langchain. Experience with DL architectures such as transformers/CNNs. Excellent programming skills in Python and object-oriented paradigm. Agile software development experience. Preferred Qualifications Experience in Biomedical More ❯

technology and business. Proficient/intermediate level programming skills, preferably Python. Software collaboration experience using version control, preferably Git. Experience using foundational data science libraries e.g., Pandas, NumPy, scikit-learn or equivalent. Experience applying state-of -the-art machine learning to commercial problems. Experience using software development/deployment tools, platforms, and best practices e.g., CI/CD More ❯

deep learning methods and machine learning - Experience in building machine learning models for business application - Experience in applied research PREFERRED QUALIFICATIONS - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. - Experience with large scale distributed systems such as Hadoop, Spark etc. - PhD Amazon is an equal opportunities employer. We believe passionately More ❯

Expert knowledge of automated artefact deployment using YAML based CI/CD pipelines and Terraform - Working knowledge of one or more ML engineering frameworks (e.g. TensorFlow, PyTorch, Keras, Scikit-Learn) - Working knowledge of object-oriented programming and unit testing in Python - Working knowledge of application and information security principles and practices (e.g. OWASP for Machine Learning) - Working knowledge More ❯

into production. Your Profile: Experience in both data engineering and machine learning, with a strong portfolio of relevant projects. Proficiency in Python with libraries like TensorFlow, PyTorch, or Scikit-learn for ML, and Pandas, PySpark, or similar for data processing. Experience designing and orchestrating data pipelines with tools like Apache Airflow, Spark, or Kafka. Strong understanding of SQL More ❯

we run a mixture of Linux and Windows, and use Python as the primary language, with an emphasis on Python and the Python scientific stack: numpy, scipy, pandas, scikit-learn, etc. We implement the systems that require the highest data throughput in Java. We implement most of our long running services and analytics in C#. We use Airflow More ❯

previous management or mentorship. Have good communication skills. Nice to have Experience deploying LLMs and agent-based systems Our technology stack Python and associated ML/DS libraries (scikit-learn, numpy, pandas, LightGBM, LangChain/LangGraph, TensorFlow, etc ) PySpark AWS cloud infrastructure: EMR, ECS, ECR, Athena, etc. MLOps: Terraform, Docker, Spacelift, Airflow, MLFlow Monitoring: New Relic CI/ More ❯

regional offices and time zones. Proficient in financial analysis, modelling, and statistics, with experience extracting insights from heterogeneous multi-dimensional datasets. Ability to apply machine learning techniques (e.g., scikit-learn, PyTorch, TensorFlow) and present complex data visually using Matplotlib, seaborn, or Streamlit. Fluent in Python, with experience working with data processing libraries such as Pandas. Strong SQL skills More ❯

to solve. The Technology Core systems are almost all running on Linux and most of the code is in Python, with the full scientific stack: numpy, scipy, pandas, scikit-learn to name a few of the libraries used extensively. For storage, they rely heavily on MongoDB. They use Docker, Kubernetes and Airflow to streamline deployments and leverage OpenFin More ❯

LLMs and generative AI. Able to contextualise these techniques in industry settings, such as financial forecasting, operations optimisation, or customer segmentation. Comfortable with key ML frameworks (such as Scikit-learn, TensorFlow, PyTorch, Hugging Face ) and data manipulation tools ( Pandas, NumPy ), as well as version control, containerisation, and ML deployment pipelines. Understands how to apply MLOps principles in production More ❯

understanding of the underlying physical principles related to battery technology, electric vehicles, EV charging, and the energy sector. Python experience (numpy, streamlit, scipy, pandas, matplotlib, plotly, poetry, GIT, scikit-learn and other scientific libraries) Excellent mathematical, analytical and problem-solving ability Excellent professional communication, reporting and presentation skills Excel proficiency Desirable but non-essential skills: Additional software engineering More ❯

run a mixture of Linux and Windows, and use Python and C# as primary languages, with an emphasis on Python and the Python scientific stack: numpy, scipy, pandas, scikit-learn, etc. We implement the systems that require the highest data throughput in Java. We implement most of our long running services and analytics in C#. We use Airflow More ❯

Job Title Senior Machine Learning Engineer Job Description Here at UnderwriteMe, we are on a mission to make life insurance more widely accessible and ensure people and their loved ones are protected when the inevitable happens. We are doing this More ❯

forecast offering. Developing our power market and dispatch models to grow and enhance Modo's product offering. We use Python and the standard scientific computing stack (Numpy, Pandas, SciPy, ScikitLearn, etc.). Working closely with our product and analytics functions to ensure the product we deliver aligns closely with user needs and provides value to the wider Modo team. Qualifications … to 5 years experience using Python (or another programming language e.g. R, C++, Java) and with the scientific computing stack (Numpy, Pandas, SciPy, ScikitLearn, etc.). Strong quantitative skills and a proven track record of solving complex technical problems using data analysis, machine learning, and optimization techniques. Hands-on experience with cloud environments (e.g., AWS) for deploying data science models. More ❯