26 to 50 of 247 Pandas Jobs in the UK

solutions Broad experience across multiple ML domains, including: Forecasting/Time-Series Modelling Regression Classification Gradient Boosting models (e.g. XGBoost, LightGBM) Strong Python skills (Pandas, NumPy, scikit-learn, PyTorch) Experience with model evaluation, performance monitoring, and accuracy metrics Version control (Git) Experience working with cloud environments (Azure preferred, AWS/ ...

learning, time series forecasting, reinforcement learning, optimization, or conversational AI. Familiarity with Python and common AI/ML frameworks (e.g. TensorFlow, PyTorch, scikit-learn, Pandas, NumPy). Software engineering fundamentals — version control, code review, modular design, and writing testable code intended for someone else to run in production. Strong analytical ...

CROs: data transfers, EDC extracts, visit structures and database lock Strong Python skills gained in a commercial setting, including the scientific Python stack e.g. pandas, NumPy, pandera and pytest Experience building and maintaining production data pipelines with automated testing and CI/CD Experience implementing data validation and quality checks ...

CROs: data transfers, EDC extracts, visit structures and database lock Strong Python skills gained in a commercial setting, including the scientific Python stack e.g. pandas, NumPy, pandera and pytest Experience building and maintaining production data pipelines with automated testing and CI/CD Experience implementing data validation and quality checks ...

from concept through to production. Strong technical knowledge, including proficiency in Python and experience with core data science and ML libraries (e.g., scikit-learn, pandas, PyTorch or TensorFlow). Practical experience with Databricks, including MLflow for experiment tracking and model management, and Spark-based data processing. Familiarity with Generative ...

Data and Python: Python for data processing, pipelines and model serving, alongside the libraries and tooling used by our data science team (such as pandas, Polars and PySpark) What we’re looking for: Significant experience as a senior or principal engineer leading the design and delivery of data‐intensive systems ...

Linux environment Nice to have: Advanced data engineering experience in Python, e.g. with libraries like Dagster, Prefect etc Experience optimising dataframe code, e.g. in Pandas or ideally Polars Experience of machine learning techniques and related libraries and frameworks e.g. scikit-learn, Pytorch, Tensorflow etc Experience deploying and serving ML models ...

skills, with a track record of building and maintaining production‐quality systems. Expert‐level Python experience with numerical, statistical, or ML libraries (e.g. NumPy, pandas, PyTorch, JAX, TensorFlow). Deep understanding of statistical modelling, optimisation, or machine‐learning techniques. Experience building and operating backend systems and data pipelines ...

Excellent communication skills with the ability to communicate concepts clearly and precisely Experience writing software using: i) Python for data science (numpy, scipy, scikit, pandas; ii) databases; and iii) cloud platform services (AWS, GCP, Azure) NICE TO HAVES Experience as a Data Architect or Senior Manager Proven track record ...

scalable SaaS platform. Any other ad hoc duties that may be required from time to time Technical Requirements: Proven experience with Python, especially Pandas and NumPy Mastery of SQL and PostgreSQL: schema design, complex queries, indexing and performance optimisation, beyond basic querying Experience with pipeline orchestration and scheduling Proven ability ...

analytical approach. Ability to work on projects independently and own deliverables. Strong proficiency in Python for data manipulation, analysis, visualisation and data science (e.g., Pandas, NumPy, Scikit‐learn). Proficiency in data querying languages (e.g., SQL). Solid understanding of software engineering best practices (version control, unit testing, code review ...

Strong understanding of core ML concepts - supervised/unsupervised learning, model evaluation, feature engineering - and broad familiarity with common algorithms and architectures. Strong Python (pandas, NumPy, etc.) and SQL skills Comfortable with the standard ML toolkit: Git, Docker, scikit-learn, PyTorch or TensorFlow, and ML pipelines. Hands-on experience with ...

vector databases, and frameworks such as LangChain/LangGraph or Hugging Face. Strong Python and SQL, with working knowledge of the wider ML stack (pandas, scikit-learn, PyTorch or TensorFlow, XGBoost). Experience with cloud data and ML platforms, ideally AWS (S3, SageMaker Pipelines, Glue) and/ ...

analytical outcomes. Desirable Well developed experience in a Lloyd’s syndicate, managing agent, London Market broker or specialty (re)insurer. Python for data analysis (pandas/notebooks) and an appreciation of wider data science techniques, enough to collaborate credibly with Data Scientists and Actuarial. Hands‐on experience applying AI/ ...

equivalentEnd-to-end experience with data, including querying, aggregation, analysis, and visualizationStrong competencies in Python for data analysis using the python data science libraries (Pandas/NumPy/SciPy/Scikit-learn).Strong SQL skills for data retrieval, basic analysis, and data cleansing.Strong knowledge and application of object-oriented design ...

design, statistical evaluation, benchmarking and model validation. Experiment tracking and reproducibility tools, for example MLflow, Weights & Biases or equivalent. Data wrangling and analysis using Pandas, NumPy, SQL; familiarity with Spark or similar is a plus. Model optimisation and research-to-prototype deployment fundamentals, including ONNX, TorchScript, FastAPI/gRPC ...

multi-threading (-s), worker processes, memory allocators, compression algorithms, and partitioned database maintenance. Languages & Frameworks: Strong proficiency in Python (including data science stack: Pandas, NumPy, and integration tools like embedPy, PyKX, or qPython). Solid understanding of Java interface programming with KDB+ and platform Familiarity with enterprise messaging middleware (Kafka ...

architecture, and security teams to ensure platform access and data privacy. Key Skills and Requirements: Proficiency in production-quality Python (e.g., FastAPI, Pydantic, asyncio, pandas, pytest). Experience with R and R Shiny for building and deploying applications. Hands-on expertise with LLM APIs (e.g., OpenAI, Anthropic Claude) and agent ...

technologies and identify opportunities to improve business efficiency through intelligent automation. Skills & Experience Strong Python programming skills with experience using libraries such as Pandas, NumPy, Scikit-learn, XGBoost or LightGBM, and Plotly, Rust, Typescript, Python or Matplotlib. Experience developing machine learning models for prediction, optimisation or classification. Strong SQL skills ...

production-grade backend systems at scale. Core Technical Skills Strong proficiency in Python as a primary backend language. Hands-on experience with FastAPI and Pandas for building data-driven API services. Solid experience with PostgreSQL - schema design, query optimization, and migrations. Experience with RabbitMQ or similar message brokers for asynchronous ...

software testing and test automation. An introduction to data science, including data analysis, statistics and visualisation. Training in Python and tools such as NumPy, Pandas and PyTest. An introduction to AI and Machine Learning, including LLMs, SLMs and traditional ML models. Specialist training in AI testing, model performance, data quality ...

Python; experience with R and/or C/C++ is a strong plus Extensive experience with numerical and data science libraries (e.g., NumPy, Pandas, SciPy, scikit-learn, PyTorch, TensorFlow, or similar) Proven experience building or maintaining machine learning systems in a distributed computing environment Proficiency developing in a Linux ...

cloud architectures and managing timelines, risks, and deployment roadmaps across multiple enterprise accounts . Python: Expert, production-quality Python (for example FastAPI, Pydantic, asyncio, pandas, pytest). R and R Shiny : Solid R and experience building and deploying Shiny applications. LLM and agent engineering: Hands-on experience with LLM APIs ...

unsupervised learning, and measurement/validation discipline Strong Python and SQL; comfortable with large transactional datasets and common DS/ML libraries (e.g. pandas, scikit‐learn, XGBoost, PyTorch or similar) Experience collaborating on MLOps‐style workflows (experiment tracking, serving, monitoring) and shipping with engineers Able to communicate uncertainty and model ...

strong foundation in system design, data storage, and reliability with commonly used data platforms (Databricks, Snowflake, BigQuery, PSQL, etc.) and data engineering tools (e.g., Pandas, Spark, dbt, etc.) Hands-on experience with MLOps/LLMOps principles, including CI/CD for data workflows, automated agent evaluation (LangSmith, Opik, Langfuse ...