1 to 25 of 185 scikit-learn Jobs

obtain). Proven experience as a Machine Learning Engineer, Data Scientist, or similar role. Strong programming skills in Python (eg Pandas, NumPy, Scikit-learn). Experience with machine learning frameworks such as TensorFlow, PyTorch, or similar. Experience building and deploying models in production environments. Knowledge of data engineering ...

stack products Strong backend (Node.js, Python/Flask, FastAPI) and frontend (React, Next.js, Vue) fundamentals ML/data science proficiency: Python, SQL, pandas, scikit-learn, PyTorch/TensorFlow Experience with cloud platforms (AWS, GCP, Azure) and containerisation (Docker, Kubernetes) Proven ability to build data-secure, privacy-compliant infrastructure ...

Skills (What we're looking for) Technical Skills: · Strong programming skills in Python with expertise in ML/Al libraries (e.g., TensorFlow, PyTorch, scikit-learn, Keras). · Experience working with large datasets and using tools such as Pandas, NumPy, and Matplotlib. · Proficiency with cloud platforms like AWS, Azure ...

engaging with non-technical stakeholders to scope, design and build an appropriate ML solution. Proficient with Python data science stack, e.g., pandas, scikit-learn, Jupyter etc., and version control, e.g., Git. Exposure to LLMOps, model monitoring principles, CI/CD and associated tech, e.g., Docker, MLflow, k8s, FastAPI ...

flows) Understanding of regulatory and compliance implications in payments Experience working in payments transformation programs Technical Strong Python skills Experience with ML libraries (Scikit-learn, TensorFlow, PyTorch, XGBoost, etc.) Experience with LLMs/GenAI frameworks (LangChain, RAG, prompt engineering, etc.) Experience in cloud (AWS/Azure/ ...

GenAI: Experience with LLM APIs and Context Engineering (constructing prompts, managing context windows, evaluating behaviour). Engineering & Stack: Python: Expert level (Pandas, NumPy, Scikit-Learn). Data Engineering: Strong SQL skills and experience building data pipelines. Experience: Education: Degree in a quantitative field (Statistics, Mathematics, Computer Science, etc. ...

experimentation, model training/deployment, RAG pipeline development, prompt engineering) and tools/languages such as Python, Spark, notebooks, and ML frameworks (e.g., scikit-learn, MLflow, TensorFlow/PyTorch, LangChain, LlamaIndex at a conceptual level). Consulting Skills · Skilled at asking the right questions to uncover technical requirements ...

experimentation, model training/deployment, RAG pipeline development, prompt engineering) and tools/languages such as Python, Spark, notebooks, and ML frameworks (e.g., scikit-learn, MLflow, TensorFlow/PyTorch, LangChain, LlamaIndex at a conceptual level). Consulting Skills · Skilled at asking the right questions to uncover technical requirements ...

Computer Science, Data Science, Statistics, Mathematics, or a related field . Strong proficiency in Python and key data science libraries ( NumPy, pandas, scikit-learn, Matplotlib, Seaborn ). Basic understanding of supervised and unsupervised learning algorithms (e.g., regression, classification, clustering). Knowledge of data querying and manipulation using ...

exposure to ML lifecycle. Hands-on experience building or maintaining ML workflows or pipelines. Strong Python skills and experience with tools like MLflow, Scikit-learn, or PyTorch. Familiarity with cloud platforms (especially Azure, AWS). Good understanding of containerisation (Docker) and orchestration (e.g. Kubernetes). Exposure ...

prioritisation What We're Looking For Strong operational research and optimisation background - this is a must Fluent Python, with hands-on experience of scikit-learn, pandas, numpy, Gurobi or similar OR packages Production ML/optimisation software experience - you've shipped models that run at scale Cloud platform ...

Machine Learning deployments on AWS, Azure or GCP Extensive hands-on data science experience, leveraging machine learning and data science tools (i.e., pandas, scikit-learn, PyTorch, etc.) Experience with DevOps tools: Kubernetes, Docker, Terraform, CI/CD Pipelines, GitHub/GitLab, GitOps, GitHub Actions, Jira, Jenkins, CircleCI, Datadog ...

Machine Learning deployments on AWS, Azure or GCP Extensive hands-on data science experience, leveraging machine learning and data science tools (i.e., pandas, scikit-learn, PyTorch, etc.) Experience with DevOps tools: Kubernetes, Docker, Terraform, CI/CD Pipelines, GitHub/GitLab, GitOps, GitHub Actions, Jira, Jenkins, CircleCI, Datadog ...

will have experience in many of the following: Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/ ...

learning technologies and best practices Skills and Experience • Strong programming skills in Python • Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn • Experience working with SQL and large datasets • Familiarity with data pipelines, feature engineering, and model optimisation • Understanding of MLOps practices, model deployment ...

Machine Learning deployments on AWS, Azure or GCP Extensive hands-on data science experience, leveraging machine learning and data science tools (i.e., pandas, scikit-learn, PyTorch, etc.) Experience with DevOps tools: Kubernetes, Docker, Terraform, CI/CD Pipelines, GitHub/GitLab, GitOps, GitHub Actions, Jira, Jenkins, CircleCI, Datadog ...

Python (or similar experience with a desire to develop Python skills) Familiarity with essential data science tools and libraries (e.g. NumPy, Pandas, Scikit-Learn) and machine learning (e.g. PyTorch, Tensorflow, Huggingface, LangChain, LiteLLM). A high level of competence in applied maths, statistics and scientific methods. Clear written ...

Python (or similar experience with a desire to develop Python skills) Familiarity with essential data science tools and libraries (e.g. NumPy, Pandas, Scikit-Learn) and machine learning (e.g. PyTorch, Tensorflow, Huggingface, LangChain, LiteLLM). A high level of competence in applied maths, statistics and scientific methods. Clear written ...

Python (or similar experience with a desire to develop Python skills) Familiarity with essential data science tools and libraries (e.g. NumPy, Pandas, Scikit-Learn) and machine learning (e.g. PyTorch, Tensorflow, Huggingface, LangChain, LiteLLM). A high level of competence in applied maths, statistics and scientific methods. Clear written ...

Python (or similar experience with a desire to develop Python skills) Familiarity with essential data science tools and libraries (e.g. NumPy, Pandas, Scikit-Learn) and machine learning (e.g. PyTorch, Tensorflow, Huggingface, LangChain, LiteLLM). A high level of competence in applied maths, statistics and scientific methods. Clear written ...

Python (or similar experience with a desire to develop Python skills) Familiarity with essential data science tools and libraries (e.g. NumPy, Pandas, Scikit-Learn) and machine learning (e.g. PyTorch, Tensorflow, Huggingface, LangChain, LiteLLM). A high level of competence in applied maths, statistics and scientific methods. Clear written ...

Python (or similar experience with a desire to develop Python skills) Familiarity with essential data science tools and libraries (e.g. NumPy, Pandas, Scikit-Learn) and machine learning (e.g. PyTorch, Tensorflow, Huggingface, LangChain, LiteLLM). A high level of competence in applied maths, statistics and scientific methods. Clear written ...

enterprise environments Deep understanding of algorithm selection, model complexity trade-offs, and overfitting/underfitting dynamics Strong proficiency in Python and ML ecosystems (scikit-learn, pandas, NumPy) Experience with deep learning frameworks (PyTorch or TensorFlow) Practical knowledge of deep learning architectures (CNNs, RNNs, Transformers) and when to apply ...

Python and its core data science libraries: Pandas, NumPy, and SciPy . Machine Learning: Deep, practical experience with popular ML frameworks and libraries: scikit-learn, TensorFlow, or PyTorch . Statistics & Math: Strong foundation in statistical modeling , probability, hypothesis testing, regression analysis, and multivariate calculus/linear algebra ...

enterprise environments Deep understanding of algorithm selection, model complexity trade-offs, and overfitting/underfitting dynamics Strong proficiency in Python and ML ecosystems (scikit-learn, pandas, NumPy) Experience with deep learning frameworks (PyTorch or TensorFlow) Practical knowledge of deep learning architectures (CNNs, RNNs, Transformers) and when to apply ...