1 to 25 of 27 Remote/Hybrid Permanent 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 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 ...

/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 ...

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. ...

lieu of professional experience Proficient in programming languages such as Python and familiar with major Data Science/machine learning libraries like OpenCV, scikit-learn, PyTorch, TensorFlow/Keras, and Pandas. Demonstrated ability to develop and test code within cloud environments. Experience with large datasets and developing ...

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 ...

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 ...

classical CV algorithms (edge detection, thresholding, color spaces). Data Analytics Fluency: Experience working with standard data manipulation libraries (NumPy, pandas, scikit-learn, SciPy). Communication: Advanced English proficiency to collaborate effectively in a global environment. Bonus Points (Nice to Have) Hands-on experience with Vision Transformers (ViTs ...

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. ...

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 ...

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 ...

Scientist with production ML exposure Strong understanding of ML algorithms, model development, and deployment Experience with Python and ML frameworks (e.g. TensorFlow, PyTorch, scikit-learn) Familiarity with data pipelines and cloud or on-prem environments Understanding of MLOps principles Ability to work in secure or regulated environments Eligibility ...

products Deep GenAI and classical ML expertise Hands-on experience with Agentic AI, evaluation frameworks, RAG and prompt engineering Strong Python skills (PyTorch, scikit-learn, modern ML frameworks) A commercial mindset, balancing tech investment with customer value The credibility to lead technically and influence at exec level Desirable ...

independently and take ownership of complex problems Strong communication skills, with the ability to explain technical concepts clearly Technical Environment Python (e.g. pandas, scikit-learn, TensorFlow/PyTorch where relevant) SQL and large-scale data processing Cloud-based environments (e.g. AWS, Azure, or similar) Experience with model deployment ...

measurable impact Experience taking models into production and managing performance over time Prior experience leading or mentoring Data Scientists Strong Python (pandas, numpy, scikit-learn or similar) Strong SQL (complex joins, aggregations, analytical functions) Solid grounding in applied statistics, evaluation design, calibration, bias/fairness Experience working closely ...

including, University degree (STEM or quantitative discipline) Strong foundations in mathematics and analytical reasoning Strong hands-on experience with, programming & Analytics: Python, SQL, Scikit-learn, TensorFlow Data & Cloud: AWS (Athena, S3, Redshift), data warehousing, ETL pipelines Visualisation & Tools: Jupyter, Visual Studio Code, Microsoft Excel, dashboards and reporting solutions ...

including, University degree (STEM or quantitative discipline) Strong foundations in mathematics and analytical reasoning Strong hands-on experience with, programming & Analytics: Python, SQL, Scikit-learn, TensorFlow Data & Cloud: AWS (Athena, S3, Redshift), data warehousing, ETL pipelines Visualisation & Tools: Jupyter, Visual Studio Code, Microsoft Excel, dashboards and reporting solutions ...

equivalent role Strong expertise in AI, machine learning, and statistical methods Proficiency in Python and ML/data analysis libraries (e.g., pandas, numpy, scikit-learn, PyTorch, transformers, statsmodels) Experience with cloud-based ML deployment (e.g., AWS services) Ability to absorb complex scientific concepts and apply them to real ...

work on Production-grade ML pipelines Model governance & lifecycle management Feature engineering at scale Embedding ML into agent-based systems Core stack Python · scikit-learn · MLflow · SQL · Docker · Feature Stores Nice to have Explainable AI · Anomaly Detection · LLM/NLP integration · Real-time inference ...

development. Some experience with Python, modern development techniques, and design patterns. Experience in data science tools and ML tools (e.g., NumPy, pandas, scikit-learn, PyTorch) and open-source contributions (especially Python-based) would be a bonus. Familiarity with CUDA, GPU-based computations, end-to-end neural network training ...

degree or higher in a STEM subject (or equivalent portfolio/work experience). You must have familiarity with ML frameworks such as scikit-learn, PyTorch and a strong grasp of statistical inference, hypothesis testing and experimental design. In your career so far, you will have ideally gained ...

This is a unique opportunity to work at the cutting edge of AI, data technology, and elite football analytics. As a Data Scientist, you’ll join a collaborative, high-performing team with a culture rooted ...

features in a regulated environment with sensitive data. You’ll work with engineering to move models from idea to production. Tech Stack: Python, scikit-learn, PyTorch, TensorFlow/Keras, LangChain, Pydantic, Spark, Kafka, Databricks, MLflow, Azure, Docker, Kubernetes. Salary: Up to £100k Working Environment: Fully Remote … What you’ll do Research, prototype, and deploy LLM use cases (Q&A, summarisation, document processing) Build and tune models using scikit-learn, PyTorch, TensorFlow/Keras, and XGBoost Create data pipelines for batch and streaming with Spark and Kafka Use Databricks and MLflow for experiments, deployment ...

problem-solving skills: Able to decompose messy, real-world problems and craft tailored solutions. Proficient in Python & data science: pandas/Polars, NumPy, scikit-learn, Jupyter, Plotly (or similar) Confident with data cleaning, analysis, transformation, and visualisation. Excellent communication: Advanced written and spoken English; concise, direct, and audience … Postgres. TypeScript fluency: Able to read TS and collaborate on data contracts with the Command team. Our environment Python, Polars/pandas, NumPy, scikit-learn, Plotly, SQL/Postgres, NVIDIA cuOpt, HERE APIs, AWS (S3, ECS/Lambda, SNS/SQS), Docker, GitHub Actions, TypeScript/Node (Command ...