1 to 25 of 104 scikit-learn Jobs

years) as a Machine Learning Engineer , Data Scientist , or similar role. Strong programming skills in Python (experience with libraries such as TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools ...

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 ...

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 ...

between data and lived football judgement Essential Requirements Technical Skills Advanced proficiency in Python and the data science stack (pandas/polars, scikit-learn, NumPy, matplotlib/plotly) Strong SQL skills ability to design schemas, write complex queries, and manage relational data across large datasets Demonstrated experience integrating ...

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 ...

Russell Group university in Data Science, Computer Science, Mathematics, Physics, Engineering, or a related discipline Strong programming skills in Python (e.g. NumPy, pandas, scikit-learn, matplotlib); R is also welcome A solid understanding of core machine learning concepts, data wrangling, and model evaluation Proficiency with SQL and experience ...

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. ...

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 ...

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 ...

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 ...

understanding of two-tower neural networks, embedding techniques, and ranking models. Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku. Experience with ML Ops, including model deployment, monitoring, and retraining ...

Proven track record of designing, developing, and deploying AI systems in production environments. Proficient in Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven ...

Science, Artificial Intelligence, Mathematics, Data Science or similar Strong programming skills in Python Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn Experience working with large datasets and building data pipelines Knowledge of cloud platforms such as AWS, GCP, or Azure Experience developing and deploying ...

strong technical ability with curiosity and a problem-solving mindset. Strong Python skills Experience building ML models using frameworks like PyTorch, TensorFlow, or scikit-learn Hands-on experience with LLMs (prompting, RAG, fine-tuning, agents) Understanding of MLOps principles and tools (e.g. MLflow, DVC, Weights & Biases) Experience deploying ...

strong technical ability with curiosity and a problem-solving mindset. Strong Python skills Experience building ML models using frameworks like PyTorch, TensorFlow, or scikit-learn Hands-on experience with LLMs (prompting, RAG, fine-tuning, agents) Understanding of MLOps principles and tools (e.g. MLflow, DVC, Weights & Biases) Experience deploying ...

SparkSQL in data or ML-focused roles. Strong understanding of machine learning concepts and experience using common ML libraries such as scikit‐learn, TensorFlow or PyTorch, alongside packages such as NumPy and Pandas. Knowledge of natural language processing techniques and API integration patterns for ML services. Experience delivering ...

Data Science, Mathematics, Statistics, Artificial Intelligence or similar Strong programming skills in Python Experience with machine learning libraries such as TensorFlow, PyTorch, or Scikit-learn Experience working with large datasets and building data pipelines Knowledge of cloud platforms such as AWS, Azure, or GCP would be beneficial Experience ...