1 to 25 of 51 Pandas Jobs

Kubernetes, Spark â Experience with Java 2D graphics and 3D OpenGL programming. â Experience with scientific computing libraries and frameworks: o Python: NumPy, SciPy, Pandas, TensorFlow (for ML/AI) o C Java: CUDA (for GPU acceleration) o Angular or React o Microservice: Quarkus, Spring Boot, AWS API Gateway

with Docker and/or Kubernetes Cloud platform knowledge (AWS preferred, but also Azure, GCP) Plusses: Experience working with data scientists Data library experience (Pandas and/or NumPy) Knowledge of microservices architecture and RESTful API design Integration experience with LangChain or similar AI frameworks to build AI based workflows

fixes. Key Skills: Investigating and debugging complex data flow and Machine Learning issues within a live, high impact production environment. Extensive Python, NumPy and Pandas is required for this role. You must demonstrate a deep commercial background in the following areas: Extensive Python: Very strong, production-level Python coding

data workflows. Hands-on experience with AWS data services, such as S3, Glue, Redshift, Lambda, and IAM. Proficiency in Python for data engineering (pandas, boto3, pyodbc, etc.). Solid understanding of data modeling, relational databases, and schema design. Familiarity with version control, CI/CD, and automation practices. Ability

Data Engineer: Extensive hands-on experience with AWS data services Strong programming skills in Python (including libraries such as PySpark or Pandas) Solid understanding of data modelling, warehousing and architecture design within cloud environments Experience building and managing ETL/ELT workflows and data pipelines at scale Proficiency with

Required: 7+ Years of professional work experience in Python software development Extensive experience with core AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn, Pandas, XGBoost) Experience with Python RESTful APIs using Fast API, ensuring effective communication between backend systems and applications Experience with relational and/or NoSQL databases

Models (GPT, Claude), Generative AI, RAG Agentic AI, CoPilot, MCPs AIML Algorithms (Regression, Classification, Decision Trees, KNN, K-Means) Python (NLTK, NumPy, Scikit-learn, Pandas) Experience building AIML solutions for test generation, prioritization, defect triage, reporting, framework migration Strong understanding of QA automation frameworks and test lifecycle SQL proficiency Agile

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

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

familiarity with core ML concepts (classification, time-series, statistical modeling) and their real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability

Qualifications Advanced proficiency in Python (specifically for machine learning) and extensive experience with core AI/ML open-source libraries, including scikit-learn, PyTorch, pandas, polars, NumPy, and seaborn. Proven experience designing and deploying end-to-end AI/ML systems, with a strong emphasis on MLOps principles and tools

mandatory: 3 years of experience in data engineering and/or data architecture 2 years of experience with Python for ETL and automation (pandas, requests, API integration). 2 years hands-on experience with SQL queries, stored procedures, performance tuning (preferable Oracle, SQL Server, MySQL) 1 year experience with

London-based Financial Services Clients. You will need: Extensive hands-on development experience with Python within Financial Services Strong background working with Pandas & NumPy Good experience with Django and Javascript Strong experience working within a risk environment Proficiency with Python libraries such as click apply for full job details

Information Systems, CIS, or related field; and 1 year of experience including SQL, Python, SAS, R, AWS Redshift, AWS S3, Jupyter Notebook, MicroStrategy, NumPy, Pandas, Airflow, S3, Tableau, and ServiceNow. $127,234. Medical, Dental, Vision. Position requires travel/relocation to various unanticipated US locations. Send resume to: Digtinctive Inc.

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

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