25 of 25 Permanent NumPy Jobs in London

managing cloud services like Google Cloud and AWS. Bonus points Startup experience Experience with Python web services (e.g. FastAPI, Django). Data engineering experience using PyData stack technologies (e.g. NumPy, Pandas, etc.) Experience building machine learning systems with LLMs, RAGs utilising embeddings. Technology Frontend: Typescript, , Vercel Backend: Go, Postgres, , Google Cloud Services: GitHub, Sentry, Stytch, OpenAI Perks & Benefits (for UK More ❯

data science projects from ideation to delivery, including business scoping and stakeholder management. Strong proficiency in Python (or R), with deep experience using modern data science libraries (e.g., Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Statsmodels). Solid foundation in SQL and data wrangling across large, complex datasets. Hands-on experience with experimentation platforms, data visualization, and dashboarding tools (e.g., Tableau More ❯

ideal candidate has demonstrable skills and experience in the following technologies. Required (ideally most of the following): The wider Python (3) data science stack and ecosystem (such as Pandas, NumPy, Jupyter notebooks, SciPy, FastAPI, Flask, Matplotlib, and similar) Core ML and DL frameworks (such as PyTorch (strongly preferred), Keras, TensorFlow, scikit-learn, and similar) Cloud compute, infrastructure, services, and deployment More ❯

up? Having added nearly 100 people to the business this year, they're looking for Quantitative Developers with proficiency in Python, and experience with the Python data libraries (Pandas, NumPy) (preferably) from the Trading world. They also are on the look out for candidates who: Have deep familiarity with Python data ecosystem Understanding of Jupyter notebooks Exposure to machine learning More ❯

up Having added nearly 100 people to the business this year, they're looking for Quantitative Developers with proficiency in Python, and experience with the Python data libraries (Pandas, NumPy) (preferably) from the Trading world. They also are on the look out for candidates who: Have deep familiarity with Python data ecosystem Understanding of Jupyter notebooks Exposure to machine learning More ❯

and machine learning techniques (supervised and unsupervised learning, natural language processing, Bayesian statistics, time-series forecasting, collaborative filtering etc) ? 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, Databricks. ? Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. ? Ability to More ❯

fundamentals, and experimental design. Experience with predictive modeling techniques such as regression, classification, clustering, or time-series forecasting. Proficiency in Python and experience with data science libraries (e.g., Pandas, NumPy, scikit-learn, XGBoost, PyTorch, TensorFlow). Strong experience with SQL and data manipulation across large datasets. Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Plotly, Tableau, or Power BI). More ❯

key. What we are looking for: We're looking for someone with a strong technical foundation, an innovative mindset, and a collaborative spirit. Technical skills (essential): Expertise in Python (NumPy, pandas, scikit-learn) and SQL Experience with EDA, data wrangling, and visualization tools (e.g., Matplotlib, Plotly, BI tools) Ability to build, evaluate, and deploy ML models (REST APIs, batch jobs More ❯

maintain in-house applications for the Derivatives trading desk using Python Implement automated testing with PyTest and ensure code quality through peer reviews Process, analyse, and manipulate data using NumPy and Pandas Manage CI/CD pipelines to ensure stable production environments Utilise Git-based workflows for collaborative development Work closely with team members and stakeholders to deliver user-focused More ❯

experience delivering data migration or integration projects , ideally with ERP/CRM systems such as Microsoft Dynamics . Strong technical skills in Python , including libraries such as Pandas and NumPy . Hands-on experience with ETL design, data warehousing, and relational databases (SQL). Advanced proficiency in SQL and data modelling techniques. Knowledge of cloud data platforms (Azure preferred, AWS More ❯

domain knowledge: understanding of financial concepts, metrics, or fintech/finance experience Experience building data pipelines, ETL processes, or handling large-scale data Familiarity with modern data processing libraries (NumPy, Polars, DuckDB) What we're looking for Beyond the technical requirements, we value engineers who: Are self-motivated and eager to learn and expand their skills Possess an analytical mindset More ❯

series forecasting, reinforcement learning, optimization, or conversational AI. Familiarity with Python or similar programming languages and common toolkits and AI/ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn, Pandas, NumPy). Strong analytical and problem-solving skills. Ability to design machine learning experiments and define model performance metrics that ensure AI outputs are aligned with business goals. Ability to work More ❯

t always work with our data, so you will need to understand how to develop your own models Strong programming skills and experience working with Python, scikit-learn, SciPy, NumPy, Pandas and Jupyter Previous experience in finance is not required, although an interest in finance and the motivation to rapidly learn more is a prerequisite for working here. Why should More ❯

leverage AI tooling to get bootstrapped quickly is a must. Core AI & Machine Learning Python Vertex AI/Hugging Face LangChain/BAML — LLM frameworks Langfuse, LangSmith — Observability Pandas, NumPy, scikit-learn, PyTorch — Data & ML stack Data & Infrastructure BigQuery — Cloud data warehouse PostgreSQL — Application data Pulumi — Infrastructure as Code (TypeScript) Google Cloud Platform (GCP) — Cloud provider GitHub Actions — CI/ More ❯

complex datasets. Desirable Experience & Skills Practical knowledge of data engineering practices (designing and building robust data pipelines) Knowledge of python and core libraries applicable to data science (e.g., pandas, numpy, statsmodel More ❯

secure, deployable AI solutions Key Skills & Experience Required Senior-level experience in data science or a quantitative field Proficient programming skills (Python preferred); familiarity with core data science libraries (NumPy, Pandas, Scikit-Learn) Experience with deep learning tools (e.g. PyTorch, TensorFlow, or similar) Strong mathematical/statistical background Familiarity with a wide range of data science methods (e.g. ML, NLP More ❯

initiatives, including price, volume, and mix analysis and margin optimisation . Tech Environment Core Skills: SQL (essential), Power BI (advantageous) Platform: Microsoft Fabric (mid-implementation) Languages: Python (pandas/numpy - desirable, not essential) Other: Salesforce & Google Analytics exposure beneficial About You Proven experience as an Insight Analyst , ideally in wholesale, distribution, or another product-heavy industry. Strong knowledge of pricing More ❯

and reaction kinetics. Interest in simulation, modeling workflows, and their application in real-world pharma/biotech contexts. Basic to intermediate Python programming skills, especially with scientific libraries (e.g., NumPy, SciPy, pandas). Nice to Have Experience with Pyomo or CasADi. Familiarity with bioprocesses or pharma case studies. Curiosity about AI, workflow automation, or cloud-based platforms. Soft Skills Proactive More ❯

fit if you: Have over 3+ years of professional experience in data science or applied analytics. Are highly skilled in Python and Jupyter notebooks. Have experience using libraries including numpy, pandas, scipy, sympy, scikit-learn, torch, tensorflow. Have a bachelor's degree in data science, statistics, computer science, or related field in the U.S., Canada, New Zealand, UK or Australia. More ❯

or personalization/recommendation. Familiarity with state-of-the-art practice in these domains Proficient in Python, and experience with machine learning and deep learning toolkits (e.g., TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas) Scientific thinking, ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals Solid written More ❯

wide best practices for data access, documentation, and reproducibility. Desired Skills/Experience 4+ years of experience in data analytics or data engineering. High proficiency in Python (e.g., pandas, NumPy, matplotlib/seaborn) and SQL essential. Hands-on experience with BI tools (ideally Power BI) advantageous Familiarity with cloud-based data platforms (ideally Azure) advantageous. Strong problem-solving skills and More ❯

ready using methodologies. Python Development - Strong proficiency in Python programming including object-oriented design, asynchronous programming, error handling, and writing clean, maintainable code. Experience with key libraries including Pandas, NumPy for data manipulation, requests and APIs for integrations, asyncio for concurrent processing, and building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise More ❯

application (e.g., regression, classification, clustering, time-series forecasting, survival analysis). Solid object-oriented programming skills in Python and hands-on expertise with the core data science stack (Pandas, Numpy, Scikit-learn, XGBoost/LightGBM). A proven ability to break down vague, complex problems into concrete, solvable steps. Excellent communication and data storytelling skills. You can build strong relationships More ❯

Present findings and recommendations to non-technical stakeholders 3. Ideal Qualifications 5+ years of applied data science or analytics experience in business settings Proficiency in Python or R (pandas, NumPy, Jupyter) and strong SQL skills Experience with data visualization tools (e.g., Tableau, Power BI) Solid understanding of statistical modeling, experimentation, and A/B testing Strong communication skills for translating More ❯

Scientist, your background will include: 3+ years industry experience in a Data Science role and a strong academic background Python Data Science Stack: Advanced proficiency in Python , including pandas , NumPy , scikit-learn , and Jupyter Notebooks . Statistical & ML Modelling: Strong foundation in statistical analysis and proven experience applying a range of machine learning techniques to solve business problems (e.g., regression More ❯