1 to 25 of 49 Pandas Jobs in the UK excluding London

analytics, process automation, and insight generation-using both proprietary and third-party/foundational models Strong Python skills, with expertise in core data science and AI/ML libraries (pandas, NumPy, scikit-learn, TensorFlow, PyTorch, etc) Familiarity with additional programming languages such as Java, JavaScript/TypeScript, or C++ is beneficial, but your core expertise is in the Python ecosystem More ❯

one or more of the following database systems - DynamoDB, DocumentDB, MongoDB Demonstrated expertise in unit testing and tools - JUnit, Mockito, PyTest, Selenium. Strong working knowledge of the PyData stack - pandas, NumPy for data manipulation; Jupyter Notebooks for experimentation; matplotlib/Seaborn for basic visualisation. Experience with data analysis and troubleshooting data-related issues. Knowledge of design patterns and software architectures More ❯

Essential skills/experience: Proven track record of delivering commercial value using supervised machine learning techniques. Advanced Python programmer, and experience with data manipulation and analysis libraries (e.g., NumPy, Pandas, scikit-learn) Advanced knowledge of data science technologies including tooling and platforms. Proficient in SQL and Git. Ability to drive projects from initiation to completion. Keen interest in emerging Machine More ❯

deploy databases and data stores to support organizational requirements. Skills/Qualifications: 4+ years of experience developing data pipelines and data warehousing solutions using Python and libraries such as Pandas, NumPy, PySpark, etc. 3+ years hands-on experience with cloud services, especially Databricks, for building and managing scalable data pipelines 3+ years of proficiency in working with Snowflake or similar More ❯

and risk analysis projects. Drive innovation and resource efficiency, contributing to the continuous development of their consulting offering and analytical capabilities as the company grows. Skills & Experience Python (incl. pandas, numpy, fastapi, dash/plotly) Database development: e.g. SQL, PostgreSQL, SQLAlchemy, data warehousing, ETL pipelines Cloud computing & DevOps: e.g. AWS (EC2, Lambda, S3), Docker, CI/CD, serverless architecture Frontend More ❯

senior client stakeholders Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks (Desirable): Azure ML, AWS Sagemaker, HuggingFace, OpenAI APIs Version More ❯

senior client stakeholders Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks (Desirable): Azure ML, AWS Sagemaker, HuggingFace, OpenAI APIs Version More ❯

senior client stakeholders Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks (Desirable): Azure ML, AWS Sagemaker, HuggingFace, OpenAI APIs Version More ❯

senior client stakeholders Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks (Desirable): Azure ML, AWS Sagemaker, HuggingFace, OpenAI APIs Version More ❯

pragmatic approach to working in cross-functional Agile squads. Your experience: Expert-level fluency in Python (required) with deep experience in ML, OR, and DS libraries (e.g. scikit-learn, pandas, numpy, Gurobi). Strong knowledge of machine learning and optimisation techniques-including supervised, unsupervised learning, and operations research methods. Solid background in software engineering for data science products: version control More ❯

data cycle. • Proven Experience working with AWS data technologies (S3, Redshift, Glue, Lambda, Lake formation, Cloud Formation), GitHub, CI/CD • Coding experience in Apache Spark, Iceberg or Python (Pandas) • Experience in change and release management. • Experience in Database Warehouse design and data modelling • Experience managing Data Migration projects. • Cloud data platform development and deployment. • Experience of performance tuning in More ❯

experience with the ability to write ad-hoc and complex queries to perform data analysis. Experience developing data pipelines and data warehousing solutions using Python and libraries such as Pandas, NumPy, PySpark, etc. You will be able to develop solutions in a hybrid data environment (on-Prem and Cloud). Hands on experience with developing data pipelines for structured, semi More ❯

stakeholders in a compelling and actionable way Mentor junior data scientists and contribute to the evolution of data science standards and practices Key Skills & Technologies Technical: Strong in Python (pandas, scikit-learn, XGBoost, LightGBM, etc.) Proficient in SQL for complex customer data extraction and manipulation Experience with customer analytics techniques: segmentation, RFM analysis, clustering, time-series, A/B testing More ❯

manage technical priorities, meet deadlines, and deliver on project objectives. Masters degree in a STEM field (maths, science, engineering etc.) or equivalent Strong programming skills in Python (e.g., NumPy, Pandas, scikit-learn, TensorFlow/PyTorch). Demonstrable experience in creating and developing Python libraries. Demonstrable experience designing, implementing and training machine learning models from scratch. Strong foundations in applied mathematics More ❯

approaches to delivery. You must be able to articulate ideas effectively and strive to constantly improve deliverables. Demonstrable depth of knowledge working with Python Experience working with data, e.g. Pandas, SQL A proactive approach to continuous learning and improvement, including the use of AI tools to support development tasks Mentoring from experienced colleagues and access to training courses will help More ❯

meaningful insights from structured and unstructured datasets of varying sizes. Track record of implementing impactful models that drive sustained business results. Proficiency in the Python data science tech stack (pandas, scikit-learn, NumPy, and visualisation libraries) Experience working in a Linux-based cloud environment (e.g. GCP, Azure, AWS). Experience using git version control. Communication, stakeholder management, and problem-solving More ❯

model evaluation Requirements: 3+ years of experience in data science or ML, ideally in biotech or healthcare Strong Python programming skills and experience with ML libraries Familiarity with Jupyter , Pandas , NumPy , and MLFlow Experience working with clinical or biological datasets is a big plus Comfortable working in a fast-paced, research-driven environment Bonus Skills: Knowledge of genomics , bioinformatics , or More ❯

model evaluation Requirements: 3+ years of experience in data science or ML, ideally in biotech or healthcare Strong Python programming skills and experience with ML libraries Familiarity with Jupyter , Pandas , NumPy , and MLFlow Experience working with clinical or biological datasets is a big plus Comfortable working in a fast-paced, research-driven environment Bonus Skills: Knowledge of genomics , bioinformatics , or More ❯

Proven hands-on expertise with GCP services (BigQuery, Cloud Storage, Dataflow) Proficiency in Power BI for creating reports and dashboards Strong Python skills, including experience with data libraries like Pandas and NumPy Solid background in ETL processes, data pipeline design, and data warehousing Detail-oriented with excellent problem-solving and communication skills Nice to Have: Knowledge of data governance, quality More ❯

basic AI algorithms and explore practical applications of AI. Overview of AI and Machine Learning Types of machine learning (supervised, unsupervised, reinforcement) Introduction to Python Libraries for AI - NumPy, Pandas, Matplotlib Scikit-learn for machine learning Building AI Models Data preprocessing Training and evaluating models Advanced Programming for AI Integration This programme aims to equip participants with the knowledge and More ❯

order to apply for the position of Quantitative Developer - Python, you'll need to meet the following criteria: Strong Python skills (for example 5+ years), including libraries like NumPy, Pandas, Polars. Experience in C++ and scripting (e.g., Bash) is a plus. Background in equities/equity derivatives trading. Problem-solving mindset, comfortable in a fast-paced, collaborative environment. Degree in More ❯

lending strategy (SME or consumer lending preferred). Strong quantitative background (degree in Maths, Engineering, Physics, or similar). Experience building and maintaining predictive credit models. Fluency in Python (Pandas, NumPy, SciPy, Matplotlib) and SQL. Experience with advanced modelling techniques (e.g., Monte Carlo, Bayesian modelling) is a plus. Strong communicator. Commercial mindset and strong instincts around risk and return. Experience More ❯

including Vertex Pipelines (or similar platforms). Technical Proficiency: in SQL and Python, along with familiarity with standard libraries used in data science such as scikit-learn, Keras, TensorFlow, pandas, NumPy, seaborn, and Matplotlib. Desirables: While not essential, the following experience would be highly beneficial: Production Experience with LLM Monitoring: Hands-on experience with monitoring LLMs in production, including familiarity More ❯

with active SC will be prioritised) Within this role, you will be responsible for: Cleaning and processing tabular data (Excel, CSV, databases) Building data transformation pipelines using Python and pandas Writing SQL queries to extract and manipulate relational data Implementing data validation and quality assurance processes Working with JSON, XML, and CSV formats Supporting metadata cataloging and reference data management More ❯

with active SC will be prioritised) Within this role, you will be responsible for: Cleaning and processing tabular data (Excel, CSV, databases) Building data transformation pipelines using Python and pandas Writing SQL queries to extract and manipulate relational data Implementing data validation and quality assurance processes Working with JSON, XML, and CSV formats Supporting metadata cataloging and reference data management More ❯