126 to 150 of 415 scikit-learn Jobs in London

understanding of computer science fundamentals (databases, software engineering, cloud computing especially AWS) and data science (machine learning processes). Proficiency in Python and frameworks such as PyTorch, TensorFlow, scikit-learn, with some knowledge of LangChain, RAGAS, and CI/CD. Growth mindset and eagerness to learn new challenges. Willingness to travel and adapt to different client projects. More ❯

is not only technically strong, but solution-oriented, strategically minded, and able to communicate insights clearly to both technical and non-technical audiences. Requirements: Advance Python (Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch) & SQL skills. (Snowflake a plus) Experience with Data warehousing and database technologies Solid machine learning experience (modelling to deployment) Cloud exposure (GCP/AWS/ More ❯

Strong python programming skills as evidenced by earlier work in data science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

in statistics, probability, and machine learning fundamentals - either through a STEM degree, formal training, or self-study. Fluency in Python and SQL, including experience with libraries like Pandas, Scikit-learn, or equivalent. Demonstrated ability to solve real-world problems pragmatically using data. Clear, structured communication - especially the ability to explain complex topics simply. A growth mindset: curious, driven More ❯

in AI/ML projects - ideally working on business solutions and workflows. Proficient with Python and libraries such as NumPy, Pandas, and one or more ML frameworks (e.g., Scikit-learn, TensorFlow, or PyTorch). Understanding of how machine learning models are trained, evaluated, and deployed. Exposure to cloud platforms (especially Azure). Interest in cybersecurity, identity management, or More ❯

Strong python programming skills as evidenced by earlier work in data science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

or a related field (or equivalent practical experience). - 2-3 years of experience in machine learning, with a strong understanding of core ML algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch). - Hands-on experience with data preprocessing, feature engineering, and model training for real-world problems. - Strong Python and Java programming skills and familiarity with NLP More ❯

or a related field (or equivalent practical experience). - 2-3 years of experience in machine learning, with a strong understanding of core ML algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch). - Hands-on experience with data preprocessing, feature engineering, and model training for real-world problems. - Strong Python and Java programming skills and familiarity with NLP More ❯

or a related field (or equivalent practical experience). - 2-3 years of experience in machine learning, with a strong understanding of core ML algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch). - Hands-on experience with data preprocessing, feature engineering, and model training for real-world problems. - Strong Python and Java programming skills and familiarity with NLP More ❯

or a related field (or equivalent practical experience). 2-3 years of experience in machine learning, with a strong understanding of core ML algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch). Hands-on experience with data preprocessing, feature engineering, and model training for real-world problems. Strong Python and Java programming skills and familiarity with NLP More ❯

Strong python programming skills as evidenced by earlier work in data science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

Strong python programming skills as evidenced by earlier work in data science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

looking for: Senior experience in data science or a quantitative academic field. Strong Python programming skills demonstrated through previous work. Proficiency with data science libraries (e.g., NumPy, Pandas, Scikit-Learn) and familiarity with deep-learning frameworks (e.g., TensorFlow, PyTorch). High mathematical competence and proficiency in statistics. Solid understanding of data science techniques such as supervised/unsupervised More ❯

and Data Scientist 's working across a variety of companies & industries Provide hands-on coding sessions and real-world project mentorship using Python and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch etc) Incorporate cloud-based tools and services (e.g., AWS, GCP, or Azure) into training to simulate modern ML engineering environments (Cloud Training can be provided to More ❯

Experience with microservice architecture, API development. Machine Learning (ML): • Deep understanding of machine learning principles, algorithms, and techniques. • Experience with popular ML frameworks and libraries like TensorFlow, PyTorch, scikit-learn, or Apache Spark. • Proficiency in data preprocessing, feature engineering, and model evaluation. • Knowledge of ML model deployment and serving strategies, including containerization and microservices. • Familiarity with ML lifecycle More ❯

we do require the ability to become a fluent Python programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

experience in Trading, Structuring, Risk or Quant role, in Financial institution, Fintech, Trading house, or Commodities house. Strong coding skills required: Python, proficiency in data science stack (Pandas, scikit-learn or equivalent), SQL. Familiarity with GUI development (Dash, Panel or equivalent). Experience designing, developing and deploying trading tools and GUIs and at least one of the following More ❯

Experience with microservice architecture, API development. Machine Learning (ML): Deep understanding of machine learning principles, algorithms, and techniques. Experience with popular ML frameworks and libraries like TensorFlow, PyTorch, scikit-learn, or Apache Spark. Proficiency in data preprocessing, feature engineering, and model evaluation. Knowledge of ML model deployment and serving strategies, including containerization and microservices. Familiarity with ML lifecycle More ❯

unfamiliar fields. Hands-on experience and solid understanding of machine learning and deep learning methods. Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas). Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals. Experience with big More ❯

including participating in RFI/RFP processes, preparing bids, and delivering presentations. Familiarity with data science platforms (e.g., Databricks, Dataiku, AzureML, SageMaker) and frameworks (e.g., Keras, TensorFlow, PyTorch, scikit-learn). Experience deploying solutions on Cloud platforms (AWS, Azure, Google Cloud) using provisioning tools like Terraform. Proven ability to deploy technologies such as Docker, Kubernetes, CI/CD More ❯

data modelling and DAX skills). Experience in Data Lake technologies (DataBricks) and/or Dataiku. Experience with Data Science and AI tools and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Knowledge of machine learning algorithms, statistical modelling, and AI-driven analytics. Data analysis, management, and visualisation expertise. Proactively manages conflicts within their workstack, clearly communicating any delays More ❯

regulations. Technical Skills required: Programming: Proficiency in Python (preferred) and/or other relevant languages (e.g., R, Java, C++). Machine Learning & AI Frameworks: Experience with TensorFlow, PyTorch, Scikit-learn, or similar libraries. Data Manipulation & Analysis: Strong skills in Pandas, NumPy, and SQL for handling and analysing large datasets. Cloud Computing: Familiarity with cloud platforms such as AWS More ❯

regulations. Technical Skills required: Programming: Proficiency in Python (preferred) and/or other relevant languages (e.g., R, Java, C++). Machine Learning & AI Frameworks: Experience with TensorFlow, PyTorch, Scikit-learn, or similar libraries. Data Manipulation & Analysis: Strong skills in Pandas, NumPy, and SQL for handling and analysing large datasets. Cloud Computing: Familiarity with cloud platforms such as AWS More ❯

experience with statistical modelling or machine learning techniques (regression analysis, clustering, classification, predictive modelling) through coursework, internships, or independent projects You are proficient in Python (especially pandas, numpy, scikit-learn, or similar libraries) and comfortable performing data analysis using Jupyter notebooks or similar tools You are comfortable writing clear, efficient SQL for extracting, cleaning, and preparing datasets, demonstrated More ❯

Experience with microservice architecture, API development. Machine Learning (ML): • Deep understanding of machine learning principles, algorithms, and techniques. • Experience with popular ML frameworks and libraries like TensorFlow, PyTorch, scikit-learn, or Apache Spark. • Proficiency in data preprocessing, feature engineering, and model evaluation. • Knowledge of ML model deployment and serving strategies, including containerization and microservices. • Familiarity with ML lifecycle More ❯