1 to 25 of 42 Matplotlib Jobs in the UK

datasets, data wrangling, and data preprocessing. Experience in data science, statistical modelling, and data analytics techniques. Experience with data analysis and visualization tools (e.g., Matplotlib, Seaborn, Tableau). Ability to work independently and lead projects from inception to deployment. Experience with big data technologies (e.g., Hadoop, Spark) and cloud platforms More ❯

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 More ❯

of mathematics and statistics. Experience with machine learning frameworks like TensorFlow, Keras, or PyTorch. Knowledge of data analysis and visualization tools (e.g., Pandas, NumPy, Matplotlib). Familiarity with big data technologies (e.g., Hadoop, Spark). Excellent problem-solving skills and attention to detail. Ability to work independently and as part More ❯

with Python libraries like Flask, FastAPI, Pandas, PySpark, PyTorch, to name a few. Proficiency in statistics and/or machine learning libraries like NumPy, matplotlib, seaborn, scikit-learn, etc. Experience in building ETL/ELT processes and data pipelines with platforms like Airflow, Dagster, or Luigi. What's important for More ❯

machine learning frameworks (e.g., TensorFlow, PyTorch). • Proficiency in Python, R, or other relevant programming languages. • Experience with data analysis and visualization tools (e.g., Matplotlib, Seaborn, Tableau). • Ability to work independently and lead projects from inception to deployment. • MSc or PhD in Computer Science, Artificial Intelligence, or related field More ❯

learn, TensorFlow, PyTorch). • Proficiency in data manipulation and analysis (e.g., Pandas, NumPy, Spark). • Knowledge of data visualization tools (e.g., Power BI, Tableau, Matplotlib). • Understanding of statistical modelling, hypothesis testing, and A/B testing. • Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies. • Ability More ❯

proficiency in data manipulation libraries (e.g., Pandas, NumPy). Experience with machine learning frameworks (e.g., Scikit-Learn, TensorFlow) and data visualization tools (e.g., Tableau, Matplotlib). Solid understanding of statistics, probability, and data-driven decision-making. Experience working with databases (SQL) and data warehousing solutions. Strong problem-solving skills and More ❯

techniques. Familiarity with actuarial methods and tools (e.g., pricing, reserving, financial forecasting). Knowledge of data visualization techniques and tools (e.g., Tableau, Power BI, matplotlib). Experience with cloud-based platforms (AWS, Azure) and big data tools (Hadoop, Spark) is a plus. Educational Background : A degree in Actuarial Science, Mathematics More ❯

the ability to clean and transform data in preparation for analysis or modelling. Data visualisation - using tools like Power BI, Tableau, or libraries like Matplotlib or Seaborn to tell stories. Statistical analysis - understanding of key statistics and distributions, hypothesis testing, and the ability to derive insights. Working with both structured More ❯

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 More ❯

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 More ❯

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 More ❯

algorithms for power grid problems on top of standard optimization libraries. Familiarity with data science libraries such as NumPy, Pandas, and visualization tools (e.g., Matplotlib, Seaborn). Experience with cloud computing platforms (e.g., Google Cloud Platform, AWS, Azure) and containerization technologies (e.g., Docker, Kubernetes). Experience contributing to open-source More ❯

Machine Learning and Data Science such as TensorFlow, PyTorch, and scikit-learn. Computer vision. Data Science and Visualisation libraries including Pandas, NumPy, scikit-learn, matplotlib, Seaborn. Cloud services used in machine learning and data science, such as Azure, OpenAI, Hugging Face, AWS ML/AI. Machine Learning Applications development life More ❯

ability to analyze and interpret large datasets, uncovering meaningful trends and insights. You are proficient in SQL and experienced in using Python (pandas, numpy, matplotlib, seaborn) for exploratory data analysis and data visualization. Big plus is practical familiarity with the big data stack (Spark, Presto/Athena, Hive). You More ❯

models, clustering algorithms, classification models and time series techniques in a production environment. Proficiency with Python and all related Data Science libraries (numpy, pandas, matplotlib, etc.), and SQL with excellent analytical and algorithmic skills. A proven record for successful implementation of translating business requirements into a technical solution. Multi-tasking More ❯

large-scale data warehouses (Snowflake, Redshift, Presto). Proficiency in data visualization tools (Databricks, PowerBI) and the Python data science ecosystem (Jupyter, Pandas, Numpy, Matplotlib). Plusses: Financial services background Degree in cybersecurity Any advanced Data bricks qualifications Have lead teams of more than 10 people Recent involvement in a More ❯

large-scale data warehouses (Snowflake, Redshift, Presto). Proficiency in data visualization tools (Databricks, PowerBI) and the Python data science ecosystem (Jupyter, Pandas, Numpy, Matplotlib). Plusses: Financial services background Degree in cybersecurity Any advanced Data bricks qualifications Have lead teams of more than 10 people Recent involvement in a More ❯

large-scale data warehouses (Snowflake, Redshift, Presto). Proficiency in data visualization tools (Databricks, PowerBI) and the Python data science ecosystem (Jupyter, Pandas, Numpy, Matplotlib). Plusses: Financial services background Degree in cybersecurity Any advanced Data bricks qualifications Have lead teams of more than 10 people Recent involvement in a More ❯

large-scale data warehouses (Snowflake, Redshift, Presto). Proficiency in data visualization tools (Databricks, PowerBI) and the Python data science ecosystem (Jupyter, Pandas, Numpy, Matplotlib). Plusses: Financial services background Degree in cybersecurity Any advanced Data bricks qualifications Have lead teams of more than 10 people Recent involvement in a More ❯

Databricks, Docker, Kubernetes, Azure Machine Learning etc. Experience in Database systems: Data Lake, Delta Lake, SQL Server etc. Data Analysis and Visualization: Pandas, NumPy, Matplotlib, Seaborn, PowerBI etc Tableaux. Experience in Microsoft Azure Cloud Environment & its services such as DevOps, Azure ML etc. Experience in Version Control Systems: Azure Repos More ❯

a team with fast turnarounds, manage workloads effectively and readily assume additional responsibilities Exposure to data analysis libraries (e.g., pandas, numpy) and visualisation tools (Matplotlib, plotly) Understanding software testing principles and frameworks Preferred skills Knowledge of financial instruments and equity markets Familiarity with cloud services Familiarity with quantitative research methodologies More ❯

or related quantitative field. Minimum of 2+ years of Python developer proficiency with quantitative analysis experience with packages such as numpy, pandas, scipy, scikitlearn, matplotlib, etc. Proficiency in Linux environment (including shell scripting). 1+ years of experience with automation frameworks in software testing (e.g., PyTest, Cucumber). Experience and More ❯

extracting insights from heterogeneous multi-dimensional datasets. Ability to apply machine learning techniques (e.g., scikit-learn, PyTorch, TensorFlow) and present complex data visually using Matplotlib, seaborn, or Streamlit. Fluent in Python, with experience working with data processing libraries such as Pandas. Strong SQL skills, a good understanding of Linux, parallel More ❯

control systems (Git/GitHub), Python package management, unit testing, and code reviews Hands-on experience with data exploration and visualization tools like Pandas, Matplotlib, Bokeh, Plotly, or Tableau Exposure to cloud platforms such as AWS Experience working collaboratively in multidisciplinary teams alongside machine learning and software engineers Contract Terms More ❯