1 to 25 of 152 Permanent scikit-learn Jobs in London

Advanced Python programming skills, with a strong emphasis on writing efficient, scalable, and maintainable code. Proven experience with TensorFlow/PyTorch/Jax, Scikit-learn, and MLOps workflows for training, deployment, and monitoring of ML models. Experience working with Polars and/or Pandas for high-performance data More ❯

Selenium). Strong knowledge of data cleaning, standardization, and normalization techniques Experience with data analysis and modeling using libraries such as Pandas, NumPy, Scikit-learn, or TensorFlow. Familiarity with SQL and database management systems (e.g., PostgreSQL, MySQL). Experience with cloud platforms (e.g., AWS, Azure, GCP) and big More ❯

Selenium). Strong knowledge of data cleaning, standardization, and normalization techniques Experience with data analysis and modeling using libraries such as Pandas, NumPy, Scikit-learn, or TensorFlow. Familiarity with SQL and database management systems (e.g., PostgreSQL, MySQL). Experience with cloud platforms (e.g., AWS, Azure, GCP) and big More ❯

in NLP or classification problems. Hands on experience with ML tools like TensorFlow, PyTorch etc. Experience with data science libraries such as NLTK, Scikit-learn, SciPy, (Sci)SpaCy etc. Excellent problem-solving and programming skills in Python Excellent communication skills Preferred Qualifications & Skills: If you have the following More ❯

experience in building production-grade machine learning systems Strong programming skills in Python, with expertise in ML frameworks such as TensorFlow, PyTorch, and scikit-learn Proven track record of developing predictive models for financial applications Deep understanding of machine learning algorithms, statistical modeling, and data processing techniques Experience More ❯

experience in building production-grade machine learning systems Strong programming skills in Python, with expertise in ML frameworks such as TensorFlow, PyTorch, and scikit-learn Proven track record of developing predictive models for financial applications Deep understanding of machine learning algorithms, statistical modeling, and data processing techniques Experience More ❯

general programming languages: Python, C++, Java, etc. Experience with deep learning, machine learning and NLP frameworks such as PyTorch (or TensorFlow), HuggingFace Transformer, Scikit-learn Experience with working in Linux A strong intuition for what makes products a joy to use Empathy for how different users will need More ❯

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 More ❯

Maths, Stats, Computer Science, Engineering, etc.) from a top university. Strong foundation in statistics, probability, and applied mathematics. Proficiency in Python (Pandas, NumPy, Scikit-learn). Experience with cloud platforms (AWS, Azure, or GCP) for data processing or model deployment. Familiarity with SQL and relational databases. Exposure to More ❯

Python and SQL, with the ability to query databases and manipulate large datasets. Proficiency in key Python libraries for data science, including Pandas, Scikit-learn, Statsmodels, NumPy, SciPy, Matplotlib, TensorFlow, and Keras. Solid understanding of machine learning techniques, such as clustering, tree-based methods, boosting, text mining, and More ❯

software engineering skills, including version control (Git), code reviews, and unit testing. Familiarity with common data science libraries and tools (e.g., NumPy, Pandas, Scikit-learn, Jupyter). Experience in setting up and managing continuous integration and continuous deployment pipelines. Proficiency with containerization technologies (e.g., Docker, Kubernetes). Experience More ❯

cycles Strong understanding of data science, machine learning concepts, and analytics capabilities Experience with AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn, etc.) Experience with MLOps practices (e.g., model deployment, monitoring, retraining workflows) for operationalizing machine learning models effectively Excellent communication skills with the ability More ❯

cycles Strong understanding of data science, machine learning concepts, and analytics capabilities Experience with AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn, etc.) Experience with MLOps practices (e.g., model deployment, monitoring, retraining workflows) for operationalizing machine learning models effectively Excellent communication skills with the ability More ❯

Proficiency in Python programming language Expertise in a programming language (Python preferred) Familiar with data processing and analysis tools such as Pandas, NumPy, Scikit-learn, etc. Knowledge of machine learning concepts such as supervised and unsupervised learning, classification, regression, clustering, dimensionality reduction, etc. Experience with applying machine learning More ❯

advisory, design & implementation. Contribute to internal initiatives such as blogs & technical forums. Requirements Substantial experience with Python and relevant libraries (e.g. Pandas, Numpy, Scikit Learn, PyTorch, Tensorflow). Experience driving ML & data science solutions into production. You can put the numbers into a business perspective - you're a More ❯

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 More ❯

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 More ❯

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 More ❯

methods to non-technical stakeholders Strong programming experience in python (R, Python, C++ optional) and the relevant analytics libraries (e.g., pandas, numpy, matplotlib, scikit-learn, statsmodels, pymc, pytorch/tf/keras, langchain) Experience with version control (GitHub) ML experience with causality, Bayesian statistics & optimization, survival analysis, design More ❯

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 More ❯

such as STEM subject or Economics). Proficiency in Python, with a solid understanding of Python libraries (such as NumPy, pandas, Polars, and scikit-learn) for data manipulation and statistical modeling. A minimum of 5 years experience working with Futures order book market data, with strong familiarity in More ❯

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). More ❯

s degree in Data Science, Statistics, Computer Science, Mathematics, or Engineering - or equivalent. Proficiency in Python and relevant data science libraries (NumPy, pandas, scikit-learn, etc.). Experience with SQL, Power BI, Git & GitHub. Strong knowledge of Machine Learning Algorithms and respective theory. Ability to work within a More ❯

Proven track record delivering ML/AI solutions in complex, real-world environments Strong Python skills and experience with key ML libraries (e.g., scikit-learn, XGBoost, PyTorch) Exposure to Generative AI technologies (e.g., LLMs, embeddings, RAG systems) Excellent communication skills and ability to engage senior stakeholders Nice to More ❯

Proven track record delivering ML/AI solutions in complex, real-world environments Strong Python skills and experience with key ML libraries (e.g., scikit-learn, XGBoost, PyTorch) Exposure to Generative AI technologies (e.g., LLMs, embeddings, RAG systems) Excellent communication skills and ability to engage senior stakeholders Nice to More ❯