1 to 25 of 106 Permanent Pandas Jobs

of Trading terminology and concepts (incl. financial derivatives), gained from experience working in a Trading or Finance environment Expert in core Python with Python scientific stack/ecosystem (including pandas, numpy, scipy, stats), and a second strongly typed language (e.g.: C#, C++, Rust or Java) Expert in application design, security, release, testing and packaging Mastery of SQL/no-SQL More ❯

frameworks including RAG, LangChain, TensorFlow, and PyTorch Practical experience with Generative AI and exposure to leading LLM platforms (Anthropic, Meta, Amazon , OpenAI) Proficiency with essential data science libraries including Pandas, NumPy, scikit-learn, Plotly/Matplotlib, and Jupyter Notebooks Knowledge of ML-adjacent technologies, including AWS SageMaker and Apache Airflow. Strong skills in data preprocessing, wrangling, and augmentation techniques Experience More ❯

graduate full stack developer to work on portal development, back-end business logic and data analytics.Experience as a Python Developer, working on data intensive numerical applications (E.g. Using Numpy, Pandas)Experience with the all stages of the software development lifecycle from product design, technical design, coding, testing, documentation, release into production and ongoing maintenance Experience working with AWS python SDK More ❯

managers, and async/await patterns - Data structures and algorithms for efficient data processing and model optimization - Error handling and debugging using try-catch blocks, logging, and debugging tools - Pandas and NumPy for data manipulation, cleaning, and numerical operations - SQLAlchemy or psycopg2 for database connectivity and ORM operations - Boto3 for AWS service integration and automation - Lambda function development with proper More ❯

managers, and async/await patterns - Data structures and algorithms for efficient data processing and model optimization - Error handling and debugging using try-catch blocks, logging, and debugging tools - Pandas and NumPy for data manipulation, cleaning, and numerical operations - SQLAlchemy or psycopg2 for database connectivity and ORM operations - Boto3 for AWS service integration and automation - Lambda function development with proper More ❯

managers, and async/await patterns - Data structures and algorithms for efficient data processing and model optimization - Error handling and debugging using try-catch blocks, logging, and debugging tools - Pandas and NumPy for data manipulation, cleaning, and numerical operations - SQLAlchemy or psycopg2 for database connectivity and ORM operations - Boto3 for AWS service integration and automation - Lambda function development with proper More ❯

DynamoDB, etc.). Skilled with Docker and container management. Strong software engineering background : code reviews, version control, optimisation, testing, and logging. Expertise in machine learning libraries such as NumPy, Pandas, TensorFlow, PyTorch, SciPy, and Dask. Proven experience linking data from multiple systems to create scalable solutions. Knowledge of cloud security best practices and experience with DevOps life cycles . Desirable More ❯

DynamoDB, etc.). Skilled with Docker and container management. Strong software engineering background : code reviews, version control, optimisation, testing, and logging. Expertise in machine learning libraries such as NumPy, Pandas, TensorFlow, PyTorch, SciPy, and Dask. Proven experience linking data from multiple systems to create scalable solutions. Knowledge of cloud security best practices and experience with DevOps life cycles . Desirable More ❯

engineering, ideally in front office finance , fintech , high-frequency trading , or technology-driven startups Expert-level Python skills - designing, debugging, and optimizing complex applications Very strong data capabilities with Pandas , NumPy , SQL , real-time data processing, and ETL Familiarity with DevOps practices including Linux , cloud platforms , CI/CD , and containerization (Docker/Kubernetes desirable) Proven ability as a live More ❯

PyTorch, ONNX, NLTK, OpenAI, langchain, llama-index,kore.ai. DL Architectures: LLM, ANN, CNN, R-CNN, RNN, GRU, LSTM, Transformers, Attention Mechanism, Tokenisers. BERT, T5, Sentence Transformers, Foundational models. Packages: Pandas, NumPy, Spark, Matplotlib, SciPy. Cloud Technologies: AWS (EC2, S3, RDS, ECS, Lambda), Azure(VM, Functions, ACR, AKS), Digital Ocean, GCP, Paperspace. Database: MySQL, PostgreSQL, MongoDB, ChromaDB, Pinecone, SQLite. Web Frameworks More ❯

Science, Engineering, Mathematics, Statistics, or a related field. 6+ years of experience in ML Engineering or Data Science (finance, fintech, or treasury a plus). Proficiency in Python-including pandas, scikit learn, TensorFlow/PyTorch, LightGBM/XGBoost-and experience with SQL. Hands on experience with cloud ML platforms (AWS SageMaker, Azure ML, or Google AI Platform). Solid understanding More ❯

engineers and contribute to best practices across the team. Key skills: Strong experience with AWS data services (S3, Redshift, Glue, Lambda, Lake Formation, CloudFormation). Proficiency in SQL, Python (Pandas), Spark or Iceberg. Experience with data warehouse design, ETL/ELT, and CI/CD pipelines (GitHub, CodeBuild). Knowledge of infrastructure as code, performance tuning, and data migration. Exposure More ❯

engineers and contribute to best practices across the team. Key skills: Strong experience with AWS data services (S3, Redshift, Glue, Lambda, Lake Formation, CloudFormation). Proficiency in SQL, Python (Pandas), Spark or Iceberg. Experience with data warehouse design, ETL/ELT, and CI/CD pipelines (GitHub, CodeBuild). Knowledge of infrastructure as code, performance tuning, and data migration. Exposure More ❯

of the art in machine learning algorithms for text analytics Demonstrated experience with machine learning frameworks such as PyTorch, Keras, Tensorflow Demonstrated experience with data visualization tools (i.e. Tableau, Pandas, D3.js, ggplot, etc) Demonstrated experience with NoSQL data stores such as MongoDB or DynamoDB Demonstrated experience using Natural Language Processing tools such as spaCy, NLTK, Stanford CoreNLP, or Gensim Natural More ❯

with interfacing with customer stakeholders to align on requirements and technical implementations Required Qualifications: Active Poly Minimum 3-5 years' experience with: Data Processing Python Libraries such as PySpark, Pandas and Numpy Experience with API development in Python using Python libraries such as FastAPI Experience with Unit Testing Frameworks in PyTest and Mocking Desired Qualifications: Experience with Python ORM tools More ❯

vision, and related technologies. Key Skills & Experience Strong hands on experience with Computer Vision frameworks (e.g., OpenCV, PyTorch, TensorFlow). Proficiency in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with More ❯

vision, and related technologies. Key Skills & Experience Strong hands on experience with Computer Vision frameworks (e.g., OpenCV, PyTorch, TensorFlow). Proficiency in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with More ❯

vision, and related technologies. Key Skills & Experience Strong hands on experience with Computer Vision frameworks (e.g., OpenCV, PyTorch, TensorFlow). Proficiency in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with More ❯

vision, and related technologies. Key Skills & Experience Strong hands on experience with Computer Vision frameworks (e.g., OpenCV, PyTorch, TensorFlow). Proficiency in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with More ❯

vision, and related technologies. Key Skills & Experience Strong hands on experience with Computer Vision frameworks (e.g., OpenCV, PyTorch, TensorFlow). Proficiency in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with More ❯

of data engineering experience in building OLTP and OLAP databases, both on-prem and in the cloud 5+ years of experience with large scale data processing using SQL, Python, Pandas and Pyspark 5+ years of experience building robust and scalable data pipelines in Databricks Lakehouse platform on any key cloud platform provider, preferably Azure cloud Experience with implementing Medallion architecture More ❯

/ML algorithms and techniques (supervised, unsupervised, reinforcement learning). Solid background in data preprocessing, wrangling, and feature engineering. Proficiency in Python (essential) and familiarity with relevant libraries (NumPy, pandas, scikit-learn, TensorFlow, PyTorch). Experience with prompt engineering and model evaluation. Deployment experience using Docker or other containerisation tools. Exposure to GPU-based environments for large-scale model training More ❯

Essential Skills and Experience: Proven ability to solve complex, real-world problems through data science and analytics. Experience coaching and reviewing work of junior team members. Strong Python skills (pandas, numpy, scikit-learn) and a solid grounding in probability and statistics. Deep knowledge of machine learning methods and their practical application. Experience managing multiple end-to-end data science projects More ❯

validation techniques such as cross-validation, bootstrapping, and assessing the statistical significance of model improvements. Excellent knowledge of data analysis, visualization, and preprocessing techniques. Familiarity with tools such as Pandas, NumPy, and Matplotlib. Experience deploying Classical and Modern Time Series Forecasting solutions using tools such as Neural Prophet, SARIMA, Chronos, etc Familiar with cloud-based machine learning platforms such as More ❯

meaningful insights from structured and unstructured datasets of varying sizes. Track record of implementing impactful models that drive sustained business results. Proficiency in the Python data science tech stack (pandas, scikit-learn, NumPy, and visualisation libraries) Experience working in a Linux-based cloud environment (e.g. GCP, Azure, AWS). Experience using git version control. Communication, stakeholder management, and problem-solving More ❯