101 to 125 of 406 scikit-learn Jobs in London

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 ❯

quantitative analysis and development. - Experience with statistical analysis, machine learning, and mathematical modeling techniques. - Familiarity with data analysis libraries (e.g., pandas, NumPy, SciPy) and machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch). - Excellent analytical and problem-solving skills, with a keen attention to detail. - Effective communication skills and ability to work collaboratively in a team environment. - Fluency 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 ❯

solutions to challenging business problems. We prioritise clean, well-tested code with a culture of documentation and knowledge sharing. Our tech stack includes GCP, Python, GitHub, PyTorch, TensorFlow, Scikit-learn, and XGBoost. With mature infrastructure and dedicated teams for Data Engineering, Analytics, and Platform Engineering, our Data Scientists enjoy high autonomy. We tackle interesting datasets, set up large More ❯

learning pipelines. Experience in training, testing, and deploying machine learning models in production environments. Familiar with statistical modeling and deep learning concepts. Practical experience with ML libraries including scikit-learn, PyTorch, TensorFlow, and Keras. Ability to write clean, modular, and scalable code for data-driven applications. Strong problem-solving mindset with a focus on performance and reliability. Your More ❯

in a quantitative field. Proven experience of large-scale data analysis and hypothesis testing. Strong proficiency in statistical analysis and predictive modeling. Proficient in Python (pandas, scipy, numpy, scikit-learn) or R (tidyverse/data.table), along with SQL. Excellent problem-solving skills and attention to detail. Strong communication skills with the ability to present complex data insights 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 ❯

reasonable industry experience, or an MS with significant industry or research experience in the field 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 data More ❯

variables Comfortable presenting back to technical and non-technical stakeholders through effective data visualisation and building of reporting frameworks Comfortable with Python data science libraries such as pandas, scikit-learn, numpy, statsmodels Strong SQL experience including analytic functions, performance tuning, data wrangling Ability to work collaboratively and proactively in a fast-paced environment alongside scientists, engineers and non 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 ❯

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 ❯

ACM/ICPC, NOI/IOI, Top Coder, Kaggle and other competitions are preferred. Strong analytical and statistical modeling skills. Experience with machine learning (Generative AI) frameworks (e.g., scikit-learn, TensorFlow, PyTorch, Langchain, Weaviate, Langgraph, LlamaIndex). Proven track record of applying data science to solve real-world problems. Excellent communication and collaboration skills. Why AI71: Proven performance More ❯

machine learning models to contribute to company growth efforts, impacting revenue and other key business outcomesAdvanced understanding of Python and the machine learning ecosystem in Python (Numpy, Pandas, Scikit-learn, LightGBM, PyTorch)Knowledge of SQL and experience with relational databasesAgile, action-oriente Nice to have Apache SparkExperience working in cloud platforms (AWS, GCP, Microsoft Azure)Relevant knowledge or More ❯

a quantitative field Deep theoretical knowledge of statistical methods and ML algorithms and their practical applications. Strong proficiency in SQL and Python, especially with core ML libraries (e.g. scikit-learn, XGBoost, SciPy, PyTorch) Extensive hands-on experience in taking advanced statistical/ML solutions from prototype to production and delivering high-impact outcomes to complex business problems Proactive 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 ❯

Python or similar programming (intermediate level). Expertise in NLP libraries such as spaCy, Transformers, Sentence Transformers, LangChain, LangGraph, LlamaIndex, BERTopic. Hands-on experience with machine learning libraries (scikit-learn, TensorFlow, PyTorch). Experience with cloud platforms (AWS, Google Cloud, Azure) and version control systems (Git). Knowledge of SQL and database management. NLP Competencies : Strong understanding of More ❯

languages, frameworks including DLT/Blockchain (e.g. Solidity/Ethereum and others), with tools for data analysis and AI development, also desired (such as Python, R, TensorFlow, PyTorch, Scikit-learn, etc); Familiar with various data sources, formats, and standards, such as APIs, JSON, XML, CSV, etc, and detail-orientated; Experience in developing and deploying applications and solutions using More ❯

Data Science) with relevant industry or research experience, or an MS with equivalent experience. Practical experience with machine learning and deep learning toolkits such as TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas. Ability to design experiments, training frameworks, and evaluate model performance aligned with business goals. Experience with big data, scalable model training, and effective communication of technical concepts More ❯

as preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms – demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. Terraform). Technology deployment– proven experience usinPg More ❯

as preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms – demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. Terraform). Technology deployment– proven experience usinPg More ❯

and Spark. Preferred Will be curious, enjoy problem solving and have empathy for the problems you are challenged with Working knowledge on data science frameworks and toolkits (Scipy, Scikit-Learn, Keras, PyTorch, Etc.) Experience with Git, Databricks and Microsoft Azure technical stack Prior exposure to Agile methodologies Experience building large scale machine learning systems Master's Degree in More ❯

and clear communication skills Nice To Have: Prior exposure with execution algos, TCA, order-routing, or market-impact modelling Knowledge of statistical or machine-learning libraries (NumPy, pandas, scikit-learn, PyTorch) Experience building distributed systems with message buses (Kafka, ZeroMQ) and asynchronous I/O Experience with cloud or on-prem orchestration and scheduling frameworks (Kubernetes, HT Condor More ❯