26 to 50 of 52 Pandas Jobs in London

automation-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 More ❯

to 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 ❯

proofs-of-concept , model monitoring , and adoption of emerging AI tech. What We’re Looking For 5+ years in data engineering, with team or project leadership experience. Advanced Python (Pandas, PyTorch, TensorFlow, Scikit-learn). Strong AWS/GCP , MySQL , and CI/CD experience; Docker/Kubernetes a plus. Excellent communication, organisation, and problem-solving skills. Passion for innovation More ❯

or similar role focused on Python coding. It is essential to have good knowledge of Python built in data types/functionality as well as associated tools such as Pandas/Polars, and web frameworks. The ideal candidate will be passionate about the latest AI/Machine Learning/Automation techniques and tools, with some exposure to web scraping tools More ❯

or similar role focused on Python coding. It is essential to have good knowledge of Python built in data types/functionality as well as associated tools such as Pandas/Polars, and web frameworks. The ideal candidate will be passionate about the latest AI/Machine Learning/Automation techniques and tools, with some exposure to web scraping tools More ❯

large, 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 ❯

users 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 More ❯

key strategic 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 More ❯

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, reliable, and More ❯

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. Have More ❯

to full time after the internship. Have experience programming in Python, C/C++, Java and/or experience with statistical analysis, numerical programming, or machine learning in Python, Pandas, R. Are an ambitious entrepreneur with a track record of exceptional intellectual achievement. Are a self starter, comfortable taking initiative and working independently in a past faced and dynamic environment. More ❯

Vision, or related technical discipline. Good knowledge of machine learning and computer vision algorithms. Familiarity with real-time imaging pipelines. Experience with scientific software packages such as PyTorch, OpenCV, Pandas, SciPy, NumPy, SciKit's, etc. Experience in standard software engineering practices including version control systems and software testing methodologies. Excellent oral and written communication skills. Analytical thinker, attentive to details More ❯

and have in-depth knowledge of how they work Python: You have built and deployed production-grade Python applications and you are familiar with data science libraries such as pandas and scikit-learn Tooling & Environment : DevOps: You have experience working with DevOps tooling, such as gitops, Kubernetes, CI/CD tools (we use buildkite) and Docker Cloud: You have worked More ❯

databases and dashboarding software such as Power BI or Tableau is important. Familiarity with programming languages such as Python or R and their associated data analysis libraries (e.g., NumPy, Pandas, scikit-learn) is a plus. Statistical Expertise: Strong understanding of statistics, including descriptive statistics, regression, probability, sampling, and hypothesis testing. Communication: Excellent written and verbal presentation skills, with the ability More ❯

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 solutions Provide More ❯

LangGraph, MCP, Google ADK or similar Build solutions for LLM fine-tuning, RAG, and multi-agent AI workflows. Build Python based solutions using key data science and ML libraries (Pandas, NumPy, Scikit-learn, PyTorch, Hugging Face, etc ) Prepare documentation and sharing solution designs, decisions, and results with both technical and non-technical audiences. Required experience: Strong Python skills PyTorch, Hugging More ❯

of-concepts and assist the business with evaluations to measure success and estimate value proposition. Essential Criteria: * Extensive experience with Python and data science Python packages (e.g. scikit-learn, pandas, numpy, etc) * Understanding of data science concepts, AI/ML models, evaluation approaches, and data science applications to enhance business processes * Proven hands-on experience in Microsoft Azure ML Studio More ❯

experience as a Data Scientist , ideally in a government or large-scale enterprise environment. Strong Python programming skills, including experience with data science and AI/ML frameworks (e.g., Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow). Experience developing and fine-tuning LLMs and working with generative AI tools. Hands-on experience with Microsoft Azure (Azure ML, Databricks, or other AI More ❯

experience as a Data Scientist , ideally in a government or large-scale enterprise environment. Strong Python programming skills, including experience with data science and AI/ML frameworks (e.g., Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow). Experience developing and fine-tuning LLMs and working with generative AI tools. Hands-on experience with Microsoft Azure (Azure ML, Databricks, or other AI More ❯

approaches. Supporting business development and pitch materials. What You’ll Bring Degree in a STEM field (Maths, CS, Engineering). Experience in data science or consulting. Strong Python/Pandas skills. Entrepreneurial mindset and proactive problem-solving. Hands-on machine learning experience. Nice to have: large datasets, data engineering, pricing/optimisation, client presentations, banking/FS experience, plus any More ❯

experiments for LLM fine-tuning, retrieval-augmented generation (RAG), and multi-agent AI workflows . Deliver production-grade Python code using advanced data science, ML, and analytics libraries (eg, Pandas, NumPy, Scikit-learn, PyTorch, Hugging Face Transformers, Pydantic ). Document and communicate AI designs, decisions, and experiment outcomes to both technical and non-technical stakeholders. Lead the design and deployment More ❯

Data 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. More ❯

management initiatives to support adoption of data-driven solutions. Essential Skills & Experience Background in Data Science with experience in project management. Proficiency in Python and the data stack (NumPy, Pandas, etc.). Experience in Microsoft Azure data science environments. Understanding of project management methodologies and change management. Ability to manage multiple projects simultaneously in a fast-paced environment. Why Join More ❯

pressure). Communicate findings effectively to both technical and non-technical stakeholders, including coaches, scouts, analysts, and traders. Skills & Attributes Strong background in Python, with experience in data manipulation (Pandas/Polars) and statistical modelling. Industry experience in a data science role focussing on sports analytics or personal projects related to football data. Knowledge of advanced football metrics (possession value More ❯

Adjust, and other MMPs Engineer features for ML models: temporal patterns, user behaviour sequences, campaign attribution Ensure data quality through comprehensive validation, deduplication, schema checks, and defensive programming Optimise pandas/Polars pipelines for performance - we process millions of events daily Handle messy real-world data: duplicates, out-of-order events, schema drift, missing fields, null handling Infrastructure & Orchestration Build … with data scientists to productionise ML workflows and model inference Requirements The ideal candidate must have... 4-6+ years building production data pipelines with demonstrable business impact Deep pandas expertise: you've built real ETL pipelines in production, understand vectorization, memory optimization, and performance patterns Deep expertise in Python and SQL with high code quality standards (e.g. ruff, mypy More ❯