76 to 100 of 190 Pandas Jobs in England

Gain unique exposure to ESG data processing and financial services technology Essential Requirements 📜 Education: Degree in Computer Science, Mathematics, or related field Technical Skills: Strong proficiency in Python (particularly Pandas) Solid SQL skills Experience with cloud infrastructure (AWS preferred) Data engineering/development experience (1+ year) Experience: Previous work in small team environments Ability to hit the ground running and More ❯

scalable workflows. Help grow and shape the data science team and its role within the wider business. What We’re Looking For Strong technical foundation with proficiency in Python (Pandas, NumPy, Scikit-learn), SQL, and cloud platforms (GCP or AWS). Experience with modern data warehouses (BigQuery, Snowflake, Redshift). Proven experience in deploying machine learning models or optimisation algorithms More ❯

needs. Communicate technical concepts clearly to technical and non-technical stakeholders. Essential Skills & Experience Hands-on experience in ML, NLP, and GenAI (LLM/SLM). Proficiency in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow). Experience with SQL/NoSQL databases, APIs, and data integration. Exposure to on-premise/open-source AI frameworks. Strong problem-solving and More ❯

needs. Communicate technical concepts clearly to technical and non-technical stakeholders. Essential Skills & Experience Hands-on experience in ML, NLP, and GenAI (LLM/SLM). Proficiency in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow). Experience with SQL/NoSQL databases, APIs, and data integration. Exposure to on-premise/open-source AI frameworks. Strong problem-solving and More ❯

s or Master’s degree in a STEM field: Statistics, Mathematics, Computer Science, Engineering, or similar. Programming proficiency: Strong experience with Python and SQL; familiarity with libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch. Machine learning expertise: Hands-on experience in supervised and unsupervised learning, with exposure to NLP, computer vision, or deep learning a plus. Data visualisation More ❯

and MLOps tooling to build scalable, reliable solutions Communicating data-driven insights and influencing both technical and business stakeholders What We’re Looking For Expert-level Python (scikit-learn, pandas, etc.), SQL, and big data platforms (e.g. Databricks) Proven experience with MLOps : deploying, monitoring, and scaling models in production Strong cloud experience (AWS, GCP, or Azure) and comfort with CI More ❯

and MLOps tooling to build scalable, reliable solutions Communicating data-driven insights and influencing both technical and business stakeholders What We’re Looking For Expert-level Python (scikit-learn, pandas, etc.), SQL, and big data platforms (e.g. Databricks) Proven experience with MLOps : deploying, monitoring, and scaling models in production Strong cloud experience (AWS, GCP, or Azure) and comfort with CI More ❯

and MLOps tooling to build scalable, reliable solutions Communicating data-driven insights and influencing both technical and business stakeholders What We’re Looking For Expert-level Python (scikit-learn, pandas, etc.), SQL, and big data platforms (e.g. Databricks) Proven experience with MLOps : deploying, monitoring, and scaling models in production Strong cloud experience (AWS, GCP, or Azure) and comfort with CI More ❯

and MLOps tooling to build scalable, reliable solutions Communicating data-driven insights and influencing both technical and business stakeholders What We’re Looking For Expert-level Python (scikit-learn, pandas, etc.), SQL, and big data platforms (e.g. Databricks) Proven experience with MLOps : deploying, monitoring, and scaling models in production Strong cloud experience (AWS, GCP, or Azure) and comfort with CI More ❯

and MLOps tooling to build scalable, reliable solutions Communicating data-driven insights and influencing both technical and business stakeholders What We’re Looking For Expert-level Python (scikit-learn, pandas, etc.), SQL, and big data platforms (e.g. Databricks) Proven experience with MLOps : deploying, monitoring, and scaling models in production Strong cloud experience (AWS, GCP, or Azure) and comfort with CI More ❯

AI requirements into scalable solutions. Essential Experience & Skills 5+ years' engineering experience, with at least 3 years in ML Ops, Data Engineering, or AI infrastructure. Strong Python engineering skills (Pandas, Numpy, Jupyter, FastAPI, SQLAlchemy). Expertise in AWS services (certification desirable). Proven experience deploying and supporting LLMs in production. Strong understanding of LLM fine-tuning (PyTorch, TensorFlow, Hugging Face More ❯

Required Skills & Experience: Bachelor's degree in Computer Science, Mathematics, Statistics, Business Administration or related field Advanced knowledge of SQL Good knowledge of Python, including popular Data Science packages (pandas, matplotlib, seaborn, numpy, sklearn) Familiarity with what is happening under the hood of popular Machine Learning algorithms Strong problem-solving skills and attention to detail Strong communication and collaboration skills More ❯

skills. Bonus skills • Experience deploying and managing applications with Azure and Docker. • Familiarity with frameworks like LangChain and Retrieval-Augmented Generation (RAG) models for AI-driven applications. • Experience with pandas for data manipulation. What’s on offer • Regular salary reviews recognising performance and contribution. • Generous annual leave: 25 days plus three days over Christmas. • Flexible hybrid working to support work More ❯

learning models at scale. Deep familiarity with core ML concepts (classification, time-series, statistical modeling) and their real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from More ❯

or similar domains with forecasting needs. Skills and competencies Essential: Experience working with Python or R in a production or research setting. Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational More ❯

or similar domains with forecasting needs. Skills and competencies Essential: Experience working with Python or R in a production or research setting. Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational More ❯

a similar role. Strong programming skills and a good understanding of software engineering principles and clean code practices. Expert-level knowledge of Python for machine learning and data manipulation (pandas, NumPy). Advanced experience with SQL for data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn More ❯

joins, CTEs, window functions, optimization techniques) Advanced skills in Microsoft Excel (pivot tables, lookups, data analysis toolpak, macros a plus) Experience writing Python for data manipulation, automation, or visualization (pandas, matplotlib, seaborn) Hands-on experience working with BigQuery or other cloud-based data warehouses (Redshift, Snowflake, etc.) and JSON data structures Strong team player and desire to support work of More ❯

and refining them to strong results. Experienced in engaging with non-technical partners to scope, design and build an appropriate ML solution. Proficient with Python data science stack, e.g., pandas, scikit-learn, Jupyter etc., and version control, e.g., Git. Knowledge of OO programming, software design, i.e., SOLID principles, and testing practices. Knowledge and working experience of AGILE methodologies. Proficient with More ❯

capacity Deep proficiency in SQL (joins, CTEs, optimization) and experience working with large-scale datasets in BigQuery, Snowflake, or Redshift Strong Python skills for analytics, automation, and data manipulation (pandas, matplotlib, seaborn) Strong understanding of data modeling, governance, and performance tuning in cloud-based environments Proven ability to lead teams, define strategy, and influence cross-functional stakeholders across technical and More ❯

and refining them to strong results. Experienced in engaging with non-technical stakeholders to scope, design and build an appropriate ML solution. Proficient with Python data science stack, e.g., pandas, scikit-learn, Jupyter etc., and version control, e.g., Git. Exposure to LLMOps, model monitoring principles, CI/CD and associated tech, e.g., Docker, MLflow, k8s, FastAPI etc. Knowledge of Langchain More ❯

in the Natural Language domain or when working with other unstructured data. • Solid understanding of Python, including writing modular Pythonic code, familiarity with core Python data structures, fluency with pandas, and experience with unit testing. • Experience managing and mentoring junior colleagues. • Experience with chatbots and working with chat data. And any experience of these would be really useful: • Hands-on More ❯

interoperability between platforms is essential. Programming & Scripting : Knowledge of Python and Rust for pre- and post-processing tasks, prototyping, and interoperability with low-code solutions. Familiarity with MATLAB or Pandas/Polar for numerical computing and data analysis, particularly for processing workflows that require complex calculations. Database Management : Proficiency in SQL for database management (such as Microsoft SQL Server, MySQL More ❯

driven investment strategy. This position sits between the engineering and the investment team. You will wear multiple hats and leverage new AI and ML technologies. Key Requirements Strong Python (pandas, NumPy, scikit-learn; PyTorch/TensorFlow a plus) and SQL skills Experience applying advanced analytics or ML to real-world data (finance, fintech, or forecasting contexts ideal) Proficiency with BI More ❯

driven investment strategy. This position sits between the engineering and the investment team. You will wear multiple hats and leverage new AI and ML technologies. Key Requirements Strong Python (pandas, NumPy, scikit-learn; PyTorch/TensorFlow a plus) and SQL skills Experience applying advanced analytics or ML to real-world data (finance, fintech, or forecasting contexts ideal) Proficiency with BI More ❯