16 to 40 of 55 Pandas Jobs

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

models for scalability, performance, and accuracy. Mentor junior engineers and review code for quality and best practices. Required Skills & Experience Strong proficiency in Python (Pandas, NumPy, Scikit-learn, FastAPI/Flask). Experience with machine learning workflows: data preprocessing, model training, evaluation, and deployment preferably using Microsoft stack - Azure

Python Programming Functions, classes & object-oriented programming List comprehensions & generators Error handling Working with virtual environments & package management (pip, venv) Data Manipulation & Analysis Libraries: pandas, numpy (optional: polars) Data cleaning & preprocessing Handling missing values Grouping, merging, pivoting & aggregations SQL knowledge Software Engineering Best Practices Version control using Git Writing modular

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

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

/ML, LLMs (GPT, Claude) and Generative AI . Proven experience with Agentic AI tools, GitHub Copilot, MCPs . Advanced Python skills – NLTK, NumPy, Pandas, Scikit-learn. Good understanding of AI/ML algorithms (Regression, Classification, Decision Trees, KNN, K-Means). Experience developing AI-enhanced test automation solutions. Solid

PostgreSQL/PostGIS for spatial data management Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data science tools (e.g., Jupyter, Pandas, NumPy) Ability to design, train, and evaluate supervised and unsupervised learning algorithms Experience working with large datasets, including data preprocessing Excellent communication skills, both written

natural language processing, computer vision, or predictive analytics Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data science tools (e.g., Jupyter, Pandas, NumPy) Ability to design, train, and evaluate supervised and unsupervised learning algorithms Strong teamwork and interpersonal skills, with a collaborative and agile mindset Proven ability

STEM subject (Mathematics, Computer Science or Engineering) Experience working in a data science or consulting role In-depth experience of working with Python and Pandas An entrepreneurial spirit and drive to work in an early stage start-up A curious mindset to understand how we can improve things and better

Were looking for a talented ML Ops Engineer to join our growing teams. Key Responsibilities Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle

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.

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.

responsible for developing reproducible pipelines for differential expression, pathway enrichment, and immune deconvolution, supporting cancer biomarker discovery and drug response analysis. Proficiency in Python (pandas, TensorFlow, MATLAB), R, and SQL (including query optimization and indexing) is required, along with experience in data cleaning, signal preprocessing, and feature extraction, with emphasis

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

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

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

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

related field (we value diverse backgrounds!) Strong foundation in software engineering principles and best practices Proficiency in Python and data science libraries (e.g., pandas, scikit-learn, PyTorch) Familiarity with version control systems and CI/CD principles Understanding of statistical models and their evaluation methodologies We encourage applications from candidates

application of data science in the IC. Expert proficiency in Python (or similar languages) and experience with data science libraries (TensorFlow, PyTorch, Pandas, NumPy). Strong experience with big data processing tools (e.g., Spark, Hadoop, AWS or Azure cloud platforms). Expertise in working with geospatial data formats (e.g., GeoTIFF

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

Claude), Generative AI, Retrieval Augmented Generation. Agentic AI, CoPilot, MCPs. AIML Algorithms(Regression, Classification, Decision Trees, KNN, K-Means) Python (NLTK, NumPy, Scikit-learn, Pandas) Candi...

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

Oracle Exadata Familiarity with SingleStore/Mysql is a big plus Experience with Python, exposure to DB connect libs and others like requests, json, pandas etc Oil and Gas Industry experience Important Point to be Noted: We are specifically looking for candidates with a strong focus on PL/

testing Build and deploy predictive ML models Mentor junior team members What you'll bring 4+ years' experience in data science Strong Python skills (pandas, numpy, PyTorch etc.) Experience with traditional ML techniques and statistical analysis Solid consulting skills - communication, problem-solving, stakeholder engagement Eligible for SC Security Check clearance

pipelines using tools like Azure DevOps, GitHub Actions, or GitLab CI, targeting Azure environments. Experience with popular Python libraries and frameworks such as Pandas, NumPy, and PySpark. Benefits Full-time, permanent contract. Competitive salary up to £60,000 per annum, depending on experience. Hybrid working model based in Aberdeen