1 to 25 of 120 scikit-learn Jobs

Skills/Knowledge/Experience Data Science & ML Supervised and unsupervised learning Time series, NLP, recommendation systems (as applicable) Programming Python (NumPy, Pandas, Scikit learn) Optional: R, SQL Data Platforms Relational & NoSQL databases Big data frameworks (Spark, Hive, Databricks) MLOps & Deployment Model lifecycle management CI/CD pipelines ...

Skills AI/Technical Strong experience building and deploying machine learning and AI solutions in production. Hands‐on experience with: Python (PyTorch, TensorFlow, scikit‐learn) Data engineering tools (SQL, Spark, Airflow or similar) LLMs, NLP, and document intelligence (e.g. RAG, embeddings) Experience with cloud platforms (AWS, Azure ...

practices for data governance, security, and MLOps on GCP Proficiency with Python and common AI/ML frameworks such as TensorFlow, PyTorch, JAX, scikit learn, and Hugging Face libraries Knowledge of MLOps and LLMOps practices-including CI/CD for models, model registry/versioning, feature stores, orchestration ...

Python, R, or SQL, and experience with various data science and machine learning libraries. Experience with libraries/frameworks like NumPy, Pandas, SciPy, Scikit‐learn, TensorFlow, PyTorch, Transformers, Langchain, Streamlit, or Gradio, among others. Experience with cloud‐based tools for production use (e.g., AWS SageMaker, AWS Bedrock, Vertex ...

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/ ...

/CD pipelines and manage model deployment using Kubernetes/OpenShift and MLOps principles. Technical Requirements Core Programming: Expert-level Python (Pandas, NumPy, scikit-learn, PyTorch/TensorFlow, FastAPI, LangChain, LlamaIndex). Generative AI: Deep experience with LLMs (Gemini, OpenAI, Claude, Llama) and evaluating model safety/performance ...

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 ...

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 ...

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 ...

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. ...

Tableau or Power BI. Published work, patents or conference presentations related to AI or data science in finance. Technical Skills Programming: Python (pandas, scikit‐learn, TensorFlow/PyTorch), R, SQL, Jupyter notebooks and version control (Git). Machine Learning: regression, classification, clustering, tree‐based models, neural networks, MLOps ...

Experience Minimum 1+ year of commercial experience in machine learning or data science Strong Python skills with experience in ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost Exposure to AWS services (e.g. S3, Lambda, SageMaker) Understanding of core ML concepts: model evaluation, validation, and experimentation Familiarity ...

production-ready data pipelines that feed ML models 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 2+ years of experience developing performant, resilient, and maintainable code 2+ years of experience with data gathering ...

production-ready data pipelines that feed ML models 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 2+ years of experience developing performant, resilient, and maintainable code 2+ years of experience with data gathering ...

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 ...

databases, visualization tools). Published research, patents, or conference presentations in AI or data science related to finance. Technical Skills Programming: Python (pandas, scikit‐learn, TensorFlow/PyTorch), R, SQL, notebooks, Git. Machine Learning: regression, classification, clustering, tree‐based models, neural networks; MLOps, model deployment, automated testing. Data ...

lifecycle, from feature engineering to deployment and monitoring Build and maintain data pipelines and production workflows using Python, TensorFlow, PyTorch, scikit-learn, AWS (S3, Lambda, SageMaker, Step Functions, Bedrock), Snowflake, and Dataiku Apply MLOps best practices, including CI/CD, automated testing, model versioning, and observability Define deployment ...

university is a great indicator, though it's your depth of applied expertise that matters most. Technically, you bring: Python fluency (NumPy, Pandas, Scikit-Learn, PyTorch) and solid TypeScript/API skills Hands-on experience with LLMs prompt engineering, evaluation, RAG architectures, safety considerations Cloud-native development ...

experience as a Machine Learning Engineer or equivalent role Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with cloud-based ML deployment (e.g., AWS SageMaker, Lambda, S3) Knowledge of MLOps/LLMOps tooling (e.g. ...

experience delivering advanced AI/ML solutions in industry or engineering environments. Strong expertise in Python and AI/ML libraries: PyTorch, TensorFlow, Scikit‐Learn, Keras, NumPy, Pandas, JAX, XGBoost, PyTorch Geometric, OpenCV, Transformers, LangChain, Pinecone . Proven hands‐on experience deploying models in production and building scalable ...

Engineer delivering predictive models into production environments, ideally for operational, supply chain, or critical infrastructure use cases. · Programming: Expert-level Python programming (pandas, scikit-learn, statsmodels, PyTorch/TensorFlow). · Platform Expertise: Deep, hands-on experience with Databricks and the AWS cloud ecosystem. · Mathematical Foundation: Strong understanding ...

such as AWS, Azure, or Google Cloud Platform 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 3+ years of experience developing performant, resilient, and maintainable code 3+ years of experience with data gathering ...

such as AWS, Azure, or Google Cloud Platform 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, Kubeflow or TensorFlow 3+ years of experience developing performant, resilient, and maintainable code 3+ years of experience with data gathering ...

such as AWS, Azure, or Google Cloud Platform 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, Kubeflow or TensorFlow 3+ years of experience developing performant, resilient, and maintainable code 3+ years of experience with data gathering ...

production-ready data pipelines that feed ML models 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 2+ years of experience developing performant, resilient, and maintainable code 2+ years of experience with data gathering ...