1 to 25 of 139 Pandas Jobs in the South East

working with Quant Trading Technology, and/or working with real-time Low Latency Market Data . Expertise in Python, including popular Python libraries for data wrangling, such as Pandas, NumPy, and SQLAlchemy. Experience working with traditional SQL databases (e.g. PostgreSQL, MySQL, SQL Server) and cloud data warehouses (e.g. Snowflake, Databricks, BigQuery, Redshift). Experience with time-series data, and More ❯

working with Quant Trading Technology, and/or working with real-time Low Latency Market Data . Expertise in Python, including popular Python libraries for data wrangling, such as Pandas, NumPy, and SQLAlchemy. Experience working with traditional SQL databases (e.g. PostgreSQL, MySQL, SQL Server) and cloud data warehouses (e.g. Snowflake, Databricks, BigQuery, Redshift). Experience with time-series data, and More ❯

working with Quant Trading Technology, and/or working with real-time Low Latency Market Data . Expertise in Python, including popular Python libraries for data wrangling, such as Pandas, NumPy, and SQLAlchemy. Experience working with traditional SQL databases (e.g. PostgreSQL, MySQL, SQL Server) and cloud data warehouses (e.g. Snowflake, Databricks, BigQuery, Redshift). Experience with time-series data, and More ❯

system performance • Implement robust risk management and stress-testing tools 🎯 What You Bring • 5+ years Java (OOP, low-latency systems) • 4+ years FX or Crypto trading experience • Python (NumPy, Pandas, SciPy) • SQL or time-series DBs • Linux & distributed systems expertise • Strong communication & market intuition ✨ Bonus Skills • KDB+/Q, Haskell, or other functional languages • Machine learning (scikit-learn, etc.) • Quant More ❯

alerting systems. Work closely with DevOps and infrastructure teams to deploy solutions in cloud and on-prem environments. Required Skills & Experience Strong proficiency in Python , including libraries such as Pandas, NumPy, and PySpark. Experience with data engineering tools (e.g., Airflow, Kafka, SQL, Parquet). Solid understanding of commodities markets , trading workflows, and financial instruments. Familiarity with cloud platforms (AWS, Azure More ❯

alerting systems. Work closely with DevOps and infrastructure teams to deploy solutions in cloud and on-prem environments. Required Skills & Experience Strong proficiency in Python , including libraries such as Pandas, NumPy, and PySpark. Experience with data engineering tools (e.g., Airflow, Kafka, SQL, Parquet). Solid understanding of commodities markets , trading workflows, and financial instruments. Familiarity with cloud platforms (AWS, Azure More ❯

system performance • Implement robust risk management and stress-testing tools 🎯 What You Bring • 5+ years Java (OOP, low-latency systems) • 4+ years FX or Crypto trading experience • Python (NumPy, Pandas, SciPy) • SQL or time-series DBs • Linux & distributed systems expertise • Strong communication & market intuition ✨ Bonus Skills • KDB+/Q, Haskell, or other functional languages • Machine learning (scikit-learn, etc.) • Quant More ❯

system performance • Implement robust risk management and stress-testing tools 🎯 What You Bring • 5+ years Java (OOP, low-latency systems) • 4+ years FX or Crypto trading experience • Python (NumPy, Pandas, SciPy) • SQL or time-series DBs • Linux & distributed systems expertise • Strong communication & market intuition ✨ Bonus Skills • KDB+/Q, Haskell, or other functional languages • Machine learning (scikit-learn, etc.) • Quant More ❯

alerting systems. Work closely with DevOps and infrastructure teams to deploy solutions in cloud and on-prem environments. Required Skills & Experience Strong proficiency in Python , including libraries such as Pandas, NumPy, and PySpark. Experience with data engineering tools (e.g., Airflow, Kafka, SQL, Parquet). Solid understanding of commodities markets , trading workflows, and financial instruments. Familiarity with cloud platforms (AWS, Azure More ❯

and more on reliability and 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 and debugging skills. Production Environment: Proven More ❯

and innate curiosity to learn new things Preferred qualifications Background in traditional finance or digital assets, ideally in trading domain Hands-on experience with Python libraries and frameworks (NumPy, Pandas, Airflow, FastAPI, Flask, SQLAlchemy) Highly proficient in asynchronous, event driven distributed systems Working knowledge of cloud-native architectures, GCP preferred Experience in Go and working with real-time data streams More ❯

and innate curiosity to learn new things Preferred qualifications Background in traditional finance or digital assets, ideally in trading domain Hands-on experience with Python libraries and frameworks (NumPy, Pandas, Airflow, FastAPI, Flask, SQLAlchemy) Highly proficient in asynchronous, event driven distributed systems Working knowledge of cloud-native architectures, GCP preferred Experience in Go and working with real-time data streams More ❯

and innate curiosity to learn new things Preferred qualifications Background in traditional finance or digital assets, ideally in trading domain Hands-on experience with Python libraries and frameworks (NumPy, Pandas, Airflow, FastAPI, Flask, SQLAlchemy) Highly proficient in asynchronous, event driven distributed systems Working knowledge of cloud-native architectures, GCP preferred Experience in Go and working with real-time data streams More ❯

learning, reinforcement learning, generative models). Support continuous model improvement and scalable MLOps deployment pipelines. TECH STACK/REQUIREMENTS Core Skills: Python, TensorFlow/PyTorch, scikit-learn, OpenCV, NumPy, Pandas Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data analytics, time-series forecasting, or computer vision experience Infrastructure: AWS/GCP/Azure, Docker, Kubernetes, MLflow More ❯

learning, reinforcement learning, generative models). Support continuous model improvement and scalable MLOps deployment pipelines. TECH STACK/REQUIREMENTS Core Skills: Python, TensorFlow/PyTorch, scikit-learn, OpenCV, NumPy, Pandas Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data analytics, time-series forecasting, or computer vision experience Infrastructure: AWS/GCP/Azure, Docker, Kubernetes, MLflow More ❯

learning, reinforcement learning, generative models). Support continuous model improvement and scalable MLOps deployment pipelines. TECH STACK/REQUIREMENTS Core Skills: Python, TensorFlow/PyTorch, scikit-learn, OpenCV, NumPy, Pandas Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data analytics, time-series forecasting, or computer vision experience Infrastructure: AWS/GCP/Azure, Docker, Kubernetes, MLflow More ❯

data structures, algorithms, and software engineering best practices Track record of designing scalable, production-grade systems Excellent problem-solving, collaboration, and communication skills Nice to Have Experience with NumPy, Pandas, Cython, or Numba Exposure to market microstructure, risk modelling, or quantitative research Experience developing and maintaining live trading bots or algo execution systems Background in mentoring or technical leadership within More ❯

data structures, algorithms, and software engineering best practices Track record of designing scalable, production-grade systems Excellent problem-solving, collaboration, and communication skills Nice to Have Experience with NumPy, Pandas, Cython, or Numba Exposure to market microstructure, risk modelling, or quantitative research Experience developing and maintaining live trading bots or algo execution systems Background in mentoring or technical leadership within More ❯

particularly in recommendation systems and deep learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. 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. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to More ❯

particularly in recommendation systems and deep learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. 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. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to More ❯

particularly in recommendation systems and deep learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. 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. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to More ❯

Proven experience (3–5+ years) as a Machine Learning Engineer , Data Scientist , or similar role. Strong programming skills in Python (experience with libraries such as TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). More ❯

Proven experience (3–5+ years) as a Machine Learning Engineer , Data Scientist , or similar role. Strong programming skills in Python (experience with libraries such as TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). More ❯

Proven experience (3–5+ years) as a Machine Learning Engineer , Data Scientist , or similar role. Strong programming skills in Python (experience with libraries such as TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). More ❯

and data science . Deep understanding of AI/ML algorithms , LLMs , GenAI , and automation frameworks. Proficiency with Python, R , and frameworks such as TensorFlow, PyTorch, scikit-learn, NumPy, pandas . Experience in data wrangling , data preprocessing , and statistical modelling. Skilled in data visualisation tools (e.g., Matplotlib, Seaborn, Tableau). Ability to work independently and lead projects from inception to More ❯