8 of 8 Seaborn Jobs in the UK excluding London

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 ❯

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 ❯

as Airflow or Luigi. Experience with message brokers (e.g., RabbitMQ, Kafka) for real-time data processing. Experience with data visualisation libraries (e.g., Matplotlib, Seaborn, Plotly) is a plus. Knowledge of ORM libraries like SQLAlchemy or Django ORM. Familiarity with containerization tools like Docker. Experience with testing frameworks like More ❯

concepts and methodologies. Data Manipulation & Analysis: Proficiency with tools like Pandas, NumPy, and Jupyter Notebooks. Experience with data visualization tools such as Matplotlib, Seaborn, or Tableau. More ❯

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 ❯

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 ❯

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 and More ❯

Proficiency in Python, SQL, and NoSQL databases (e.g., PostgreSQL, MongoDB). Experience with data visualisation tools (Tableau, Power BI, or Python libraries like Seaborn). Familiarity with FastAPI for system integration and automation. API Integration Salesforce (or tools like MuleSoft) for CRM data extraction. Email APIs (e.g., Microsoft More ❯