1 to 25 of 38 Permanent 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 ❯

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 ❯

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 ❯

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 ❯

with relevant analytical and machine learning libraries (e.g., pandas, polars, numpy, sklearn, TensorFlow/Keras, PyTorch, etc.), in addition to visualization and API libraries (matplotlib, plotly, streamlit, Flask, etc). Experience developing and implementing quantitative models from data in a financial context. Proficiency working with version control systems such as 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

DAX Studio, Measure Killer) Proficiency in other data visualisation tools beyond Power BI (e.g., Tableau, Looker, ThoughtSpot), as well as data visualisation libraries (e.g., Matplotlib, Seaborn, Plotly, ggplot2, D3.js, Kepler.gl, Apache Superset, Streamlit). Expertise in data storytelling techniques and the ability to translate data into actionable insights through compelling 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 ❯

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 ❯

need Excellent SQL skills. A drive to solve problems using data. Proficiency with the Python data science stack (pandas, NumPy, Jupyter notebooks, Plotly/matplotlib, etc.). Bonus skills include: Familiarity with Git. Experience with data visualization tools (Tableau, Looker, PowerBI, or equivalent). Knowledge of DBT. 2-5 years More ❯

data analysis and visualization a meticulous coder with an eye for readability, with experience in python and industry standard data science packages (numpy, pandas, matplotlib, sqlite, or others). Able to use SQL syntax writing and workflows. You have good familiarity with file/data formats, such as markdown, json 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 ❯

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 ❯

developing and deploying machine learning models in production. Strong programming skills in Python and SQL. Proficiency with data visualization tools (e.g., Tableau, Power BI, matplotlib) and cloud platforms (preferably AWS). Expertise in statistics and machine learning frameworks (e.g., PyTorch, scikit-learn). Familiarity with Git and collaborative workflows, such More ❯