1 to 25 of 77 Matplotlib Jobs in London

frameworks (TensorFlow, PyTorch, scikit-learn) - Skilled in handling large datasets, data wrangling, and statistical modelling - Comfortable working independently on end-to-end ML pipelines - Experienced in visualisation tools (e.g. Matplotlib, Seaborn, Tableau) Desirable: - Exposure to cloud platforms (AWS, GCP, Azure) and big data tools (Hadoop, Spark) - MSc/PhD in Computer Science, AI, Data Science, or related field More ❯

and predictive modeling. Experience with NLP techniques for text analysis, classification, and information extraction. Knowledge of deep learning frameworks such as PyTorch or TensorFlow. Experience with data visualization tools (Matplotlib, Seaborn, Plotly, or similar). Strong analytical mindset with a focus on solving real-world problems. Excellent communication skills to present findings to technical and non-technical stakeholders. Fluent in More ❯

to validate models and algorithms. Data Manipulation & Analysis: Proficient in data manipulation and analysis using tools like Pandas, NumPy, and Jupyter Notebooks. Experience with data visualization tools such as Matplotlib, Seaborn, or Tableau to communicate insights effectively. Our offer: Cash: Depends on experience Equity : Generous equity package, on a standard vesting schedule Impact & Exposure : Work at the leading edge of More ❯

scikit-learn. Skilled in data wrangling, preprocessing, and managing large-scale datasets. Solid understanding of statistical modelling, predictive analytics, and time series analysis. Experience with data visualisation tools like Matplotlib, Seaborn, or Tableau. Salary: £525 per day #J-18808-Ljbffr More ❯

production-grade code in Python. Solid understanding and proficiency in working with Pandas, NumPy, Scikit-Learn, TensorFlow, and PyTorch, SQL. Proficiency in advanced data visualization tools and libraries (e.g., Matplotlib, Seaborn, Plotly, Tableau) for creating insightful and interactive visualizations. Solid understanding of data structures, data modelling, and software architecture. Experience with cloud services (such as AWS) and tools for machine More ❯

Clustering Dimensionality reduction LLM Proficiency in Python for developing machine learning models and conducting statistical analyses Strong understanding of data visualization tools and techniques (e.g., Python libraries such as Matplotlib, Seaborn, Plotly, etc.) and the ability to present data effectively Specific technical requirements: Data Science or AI/ML strategy Data Science or AI/ML solution architecture Proficiency in More ❯

Python programming skills, including experience with relevant analytical and machine learning libraries (e.g., pandas, polars, numpy, sklearn, TensorFlow/Keras, PyTorch, etc.), in addition to visualisation and API libraries (matplotlib, plotly, streamlit, Flask, etc). Understanding of Gen AI models, Vector databases, Agents, and follow the market trends. Its desirable to have a hands-on experience on these. Substantial experience More ❯

Python programming skills, including experience with relevant analytical and machine learning libraries (e.g., pandas, polars, numpy, sklearn, TensorFlow/Keras, PyTorch, etc.), in addition to visualisation and API libraries (matplotlib, plotly, streamlit, Flask, etc). Understanding of Gen AI models, Vector databases, Agents, and follow the market trends. Its desirable to have a hands-on experience on these. Substantial experience More ❯

Python programming skills, including experience with relevant analytical and machine learning libraries (e.g., pandas, polars, numpy, sklearn, TensorFlow/Keras, PyTorch, etc.), in addition to visualisation and API libraries (matplotlib, plotly, streamlit, Flask, etc). Understanding of Gen AI models, Vector databases, Agents, and follow the market trends. Its desirable to have a hands-on experience on these. Substantial experience More ❯

s degree from a 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 handling large datasets A passion for solving complex More ❯

s degree from a 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 handling large datasets A passion for solving complex More ❯

probability, bayesian stats etc.) Good working knowledge of Python Implementing end-to-end ML pipelines Hand-on experience in Python data science ecosystem (e.g. numpy, scipy, pandas, scikit-learn, matplotlib etc.) Deep Learning frameworks (TensorFlow, PyTorch, MLX) Popular classification and regression techniques Unsupervised learning & matrix factorisation algorithms Natural Language Processing (NLP) and document processing Generative AI (open and closed source More ❯

knowledge of data science fundamentals (Machine Learning methods, Statistics). Fluent in common analytics tools (Python, Pandas, Numpy, ScikitLearn, SQL, etc.) Comfortable to use data visualization libraries (e.g. Seaborn, Matplotlib) Demonstrated initiative, judgment and discretion while handling sensitive information Preferred Qualifications: If you have the following characteristics, it would be a plus: PhD - in a STEM subject and experience in More ❯

learn, XGBoost, LightGBM, StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud More ❯

learn, XGBoost, LightGBM, StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud More ❯

large datasets. Cloud Computing: Familiarity with cloud platforms such as AWS, Azure, or Google Cloud for AI model deployment. Data Visualisation: Ability to present insights effectively using tools like Matplotlib, Seaborn, Tableau, or Power BI. Model Deployment: Understanding of MLOps principles, APIs, and containerization (Docker, Kubernetes) for productionizing AI models. Business-Focused AI Application: Ability to translate AI solutions into More ❯

large datasets. Cloud Computing: Familiarity with cloud platforms such as AWS, Azure, or Google Cloud for AI model deployment. Data Visualisation: Ability to present insights effectively using tools like Matplotlib, Seaborn, Tableau, or Power BI. Model Deployment: Understanding of MLOps principles, APIs, and containerization (Docker, Kubernetes) for productionizing AI models. Business-Focused AI Application: Ability to translate AI solutions into More ❯

large datasets. Cloud Computing: Familiarity with cloud platforms such as AWS, Azure, or Google Cloud for AI model deployment. Data Visualisation: Ability to present insights effectively using tools like Matplotlib, Seaborn, Tableau, or Power BI. Model Deployment: Understanding of MLOps principles, APIs, and containerization (Docker, Kubernetes) for productionizing AI models. Business-Focused AI Application: Ability to translate AI solutions into More ❯

and learning more about key areas/topics in AI research. Python for Data Analysis: Proficiency in Python and common data analysis libraries (e.g., Pandas, NumPy, SciPy, Scikit-learn, Matplotlib/Seaborn). Data Visualization/Dashboarding: Experience creating effective dashboards and visualizations using tools like Tableau, Looker, Google Data Studio, or similar. Analytics Engineering: Experience designing and implementing ELT More ❯

and data science solutions for clients.+ Strong proficiency in programming languages such as Python, R, and SQL.+ In-depth experience with data manipulation and visualization libraries (e.g., Pandas, NumPy, Matplotlib, etc.).+ Solid understanding of big data technologies (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure, Google Cloud).+ Strong expertise in the full data science lifecycle: data collection, preprocessing More ❯

preferred). Experience in data preprocessing techniques, feature engineering, and model evaluation metrics. Proven ability to manipulate, query and visualise data and training/evaluation results (using e.g., Pandas, Matplotlib, Seaborn). Proven understanding of Information Extraction and Retrieval techniques. Proven understanding of NLP and large language models. Proven understanding of database concepts (differences between graph/relational/non More ❯

plus). Experience with version control (git) and testing. Strong foundation in statistics and probability. Knowledge of data science algorithms: classification, regression, clustering. Data visualization skills using libraries like matplotlib, seaborn, plotly. Data management skills: pandas, numpy, scipy. Experience with AWS cloud services (SageMaker, S3, EC2, EMR, Glue). Experience with LLM frameworks (LangChain, LangGraph) is a plus. What We More ❯

with a good understanding of best practices in software engineering and data engineering • Practical object-oriented programming experience in Python with knowledge of relevant packages including Pandas, NumPy, SciPy, Matplotlib, Scikit-learn, Pytorch • In-depth knowledge of statistical and machine learning models as well as experience with end-to-end delivery lifecycles • Experience in writing clean and maintainable code for More ❯

business problems Proven experience in translating technical 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 of experiments, longitudinal analysis, surrogate More ❯

series forecasting. Data Pipelines : Experience in building and maintaining efficient data pipelines to support data science initiatives. Data Visualization : Expertise in using visualization tools (e.g., Power BI, Tableau, or matplotlib) to present complex data insights. End-to-End Project Delivery : Proven experience taking a data science project from concept to deployment, including model evaluation and ongoing monitoring. Familiarity with cloud More ❯