51 to 75 of 178 NumPy Jobs in the UK

deep learning, reinforcement learning, generative models). Support continuous model improvement and scalable MLOps deployment pipelines. TECH STACK/REQUIREMENTS Core Skills: Python, TensorFlow/PyTorch, scikit-learn, OpenCV, NumPy, Pandas Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data analytics, time-series forecasting, or computer vision experience Infrastructure: AWS/GCP/Azure, Docker, Kubernetes More ❯

experience, ideally in Treasury or from a hedge fund/buy-side firm Expertise in Python for designing and optimising complex applications Strong proficiency in data tools like Pandas, NumPy, SQL, and real-time data processing Familiarity with DevOps practices, including Linux, cloud platforms, and CI/CD (Docker/Kubernetes is desirable but not essential). Strong communication skills More ❯

experience, ideally in Treasury or from a hedge fund/buy-side firm Expertise in Python for designing and optimising complex applications Strong proficiency in data tools like Pandas, NumPy, SQL, and real-time data processing Familiarity with DevOps practices, including Linux, cloud platforms, and CI/CD (Docker/Kubernetes is desirable but not essential). Strong communication skills More ❯

months with possible extensions (No Sponsorship Available ) 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 cloud-based More ❯

libraries (Matplotlib, Seaborn.) SQL for data extraction and manipulation. Experience working with large datasets. Technical Skills Proficiency in cloud computing and python programming. Familiarity with Python libraries like Pandas, NumPy, scikit-learn. Experience with cloud services for mode training and deployment. Machine Learning Fundamentals Statistical concepts for robust data analysis. Linear algebra principles for modelling and optimisation. Calculus for optimising More ❯

libraries (Matplotlib, Seaborn.) SQL for data extraction and manipulation. Experience working with large datasets. Technical Skills Proficiency in cloud computing and python programming. Familiarity with Python libraries like Pandas, NumPy, scikit-learn. Experience with cloud services for mode training and deployment. Machine Learning Fundamentals Statistical concepts for robust data analysis. Linear algebra principles for modelling and optimisation. Calculus for optimising More ❯

in recommendation systems and deep learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch. Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work More ❯

constraints. Automate Data Pipelines, designing and managing workflows for collecting, cleaning, and storing large volumes of financial data (e.g., price, volume, fundamentals, alternative data), often using tools like Pandas, NumPy, and Dask. Collaborate Across Teams for Deployment, working with researchers, traders, and DevOps teams to integrate Python models into production environments (e.g., through APIs, microservices, or containerized systems like Docker More ❯

and version control processes. Stay up to date with advances in quantitative finance, computational techniques, and emerging technologies. Profile Strong programming experience in Python, C++, or C#; knowledge of NumPy, Pandas, and QuantLib advantageous. Solid understanding of mathematics, statistics, and numerical methods - including stochastic calculus, Monte Carlo simulation, and optimisation. Familiarity with derivatives pricing, risk metrics, and financial instruments across More ❯

pipelines. Required Skills and Qualifications Core Technical Skills Skill Area Requirements Programming Strong proficiency in Python for data manipulation and scripting. Familiarity with standard Python data libraries (e.g., Pandas, NumPy ). Database Expert-level proficiency in SQL (Structured Query Language). Experience writing complex joins, stored procedures, and performing performance tuning. Big Data Concepts Foundational understanding of Big Data architecture More ❯

pipelines. Required Skills and Qualifications Core Technical Skills Skill Area Requirements Programming Strong proficiency in Python for data manipulation and scripting. Familiarity with standard Python data libraries (e.g., Pandas, NumPy ). Database Expert-level proficiency in SQL (Structured Query Language). Experience writing complex joins, stored procedures, and performing performance tuning. Big Data Concepts Foundational understanding of Big Data architecture More ❯

pipelines. Required Skills and Qualifications Core Technical Skills Skill Area Requirements Programming Strong proficiency in Python for data manipulation and scripting. Familiarity with standard Python data libraries (e.g., Pandas, NumPy ). Database Expert-level proficiency in SQL (Structured Query Language). Experience writing complex joins, stored procedures, and performing performance tuning. Big Data Concepts Foundational understanding of Big Data architecture More ❯

learning, neural networks, NLP, etc.). Hands-on experience with frameworks such as TensorFlow , PyTorch , or scikit-learn . Proficiency in Python and familiarity with common data science libraries (NumPy, pandas, etc.). Solid grasp of statistics, linear algebra, and probability. Excellent problem-solving skills and ability to communicate complex ideas clearly. Desirable Skills Experience with deep learning architectures (CNNs More ❯

implementing, and maintaining MLOps processes in a cloud environment (e.g., Azure, AWS, GCP). Technical Skills: Expertise in Python and its ML ecosystem (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy). Strong background in statistical analysis, algorithm design, and software engineering best practices. Experience with Docker and Kubernetes for containerization and orchestration. Proficiency with modern version control systems (Git). More ❯

orchestration tools such as Airflow, Prefect, or Temporal, or by building bespoke pipeline systems for multi-step autonomous processes. You bridge science and engineering — comfortable with scientific computing libraries (NumPy, SciPy, pandas) and familiar with scientific databases and literature formats. What Sets You Apart: You have a research background — perhaps as a former academic researcher or research software engineer in More ❯

orchestration tools such as Airflow, Prefect, or Temporal, or by building bespoke pipeline systems for multi-step autonomous processes. You bridge science and engineering — comfortable with scientific computing libraries (NumPy, SciPy, pandas) and familiar with scientific databases and literature formats. What Sets You Apart: You have a research background — perhaps as a former academic researcher or research software engineer in More ❯

machine learning or AI techniques Strong programming skills in Python and TypeScript (this is essential) Experience with common Python data/ML libraries (e.g. PyTorch, TensorFlow, scikit-learn, pandas, NumPy) Experience building or contributing to TypeScript codebases (e.g. Node.js backends, React frontends, or internal tools) Hands-on exposure to AWS (e.g. EC2, S3, IAM; bonus points for Lambda, ECS/ More ❯

machine learning or AI techniques Strong programming skills in Python and TypeScript (this is essential) Experience with common Python data/ML libraries (e.g. PyTorch, TensorFlow, scikit-learn, pandas, NumPy) Experience building or contributing to TypeScript codebases (e.g. Node.js backends, React frontends, or internal tools) Hands-on exposure to AWS (e.g. EC2, S3, IAM; bonus points for Lambda, ECS/ More ❯

Role Requirements 2-4 years' experience in applied machine learning and generative AI, including work with large language models. Strong Python programming skills with experience in core ML libraries (numpy, pandas, scikit-learn, boosting methods). Proven ability to design, test, and deploy production-ready machine learning solutions. Hands-on experience in data processing and feature engineering for complex datasets. More ❯

Provide data-driven recommendations to improve engagement metrics Requirements Experience in Customer Marketing Data Science, including applied statistics and machine learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku, Databricks Experience with ML Ops, including deployment and monitoring Ability to work cross-functionally with More ❯

Provide data-driven recommendations to improve engagement metrics Requirements Experience in Customer Marketing Data Science, including applied statistics and machine learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku, Databricks Experience with ML Ops, including deployment and monitoring Ability to work cross-functionally with More ❯

Stay up-to-date with emerging trends and technologies in the field of data science. Requirements Proven experience as a data scientist using Python and a range of libraries (Numpy, Pandas, Scikit-Learn, Matplotlib, Plotly etc.). Strong expertise in statistical modelling, machine learning, and data mining techniques. Data engineering (pipelines, databases, infrastructure), ideally with AWS experience would be an More ❯

experience applying statistics and data science in a commercial setting. Proven track record in customer or marketing analytics - understanding acquisition, engagement, churn, and lifetime value. Proficiency in Python (Pandas, NumPy, Scikit-learn, Statsmodels) for data wrangling, analysis, and modelling. Ability to communicate complex findings in a clear, business-relevant way. Experience working collaboratively in agile, cross-functional teams. Please note More ❯

experience applying statistics and data science in a commercial setting. Proven track record in customer or marketing analytics - understanding acquisition, engagement, churn, and lifetime value. Proficiency in Python (Pandas, NumPy, Scikit-learn, Statsmodels) for data wrangling, analysis, and modelling. Ability to communicate complex findings in a clear, business-relevant way. Experience working collaboratively in agile, cross-functional teams. Please note More ❯

For: Essential: Experience with LLMs (GPT, BERT) and NLP tasks. Fine-tuning pre-trained models for domain-specific applications. Strong Python skills (OOP, PyTorch, Hugging Face, scikit-learn, Pandas, NumPy). Deploying models with Docker, Kubernetes, or serverless platforms. Familiarity with CI/CD, MLOps, and cloud platforms (AWS preferred). Desirable: Named entity recognition, recommendation systems, or image analysis. More ❯