1 to 25 of 100 Pandas Jobs in Yorkshire

AWS Sagemaker, and Azure Machine Learning Experience in relevant Data Manipulation, Machine Learning and Statistical Analysis coding packages (eg. in Python: NumPy, Scikit-Learn, Pandas, Matplotlib etc.) Strong skills in data exploration, cleansing, modelling and presentation Strong experience in testing data models and Machine Learning Models Strong experience in data More ❯

Science, Engineering, or a related field (or equivalent practical experience). Technical Skills: Deep expertise in Python, including libraries such as Django, Flask, FastAPI, Pandas and NumPy. Strong knowledge of relational and non-relational databases (e.g., PostgreSQL, MongoDB, Redis). Proficiency with cloud platforms (e.g., AWS, Azure, GCP) and containerization More ❯

Science, Engineering, or a related field (or equivalent practical experience). Technical Skills: Deep expertise in Python, including libraries such as Django, Flask, FastAPI, Pandas and NumPy. Strong knowledge of relational and non-relational databases (e.g., PostgreSQL, MongoDB, Redis). Proficiency with cloud platforms (e.g., AWS, Azure, GCP) and containerization More ❯

Science, Engineering, or a related field (or equivalent practical experience). Technical Skills: Deep expertise in Python, including libraries such as Django, Flask, FastAPI, Pandas and NumPy. Strong knowledge of relational and non-relational databases (e.g., PostgreSQL, MongoDB, Redis). Proficiency with cloud platforms (e.g., AWS, Azure, GCP) and containerization More ❯

Science, Engineering, or a related field (or equivalent practical experience). Technical Skills: Deep expertise in Python, including libraries such as Django, Flask, FastAPI, Pandas and NumPy. Strong knowledge of relational and non-relational databases (e.g., PostgreSQL, MongoDB, Redis). Proficiency with cloud platforms (e.g., AWS, Azure, GCP) and containerization More ❯

Science, Engineering, or a related field (or equivalent practical experience). Technical Skills: Deep expertise in Python, including libraries such as Django, Flask, FastAPI, Pandas and NumPy. Strong knowledge of relational and non-relational databases (e.g., PostgreSQL, MongoDB, Redis). Proficiency with cloud platforms (e.g., AWS, Azure, GCP) and containerization More ❯

Science, Engineering, or a related field (or equivalent practical experience). Technical Skills: Deep expertise in Python, including libraries such as Django, Flask, FastAPI, Pandas and NumPy. Strong knowledge of relational and non-relational databases (e.g., PostgreSQL, MongoDB, Redis). Proficiency with cloud platforms (e.g., AWS, Azure, GCP) and containerization More ❯

Science, Engineering, or a related field (or equivalent practical experience). Technical Skills: Deep expertise in Python, including libraries such as Django, Flask, FastAPI, Pandas and NumPy. Strong knowledge of relational and non-relational databases (e.g., PostgreSQL, MongoDB, Redis). Proficiency with cloud platforms (e.g., AWS, Azure, GCP) and containerization More ❯

availability and integrity. Essential: • 3+ years of experience in data engineering, data architecture, or similar roles. • Expert proficiency in Python, including popular data libraries (Pandas, PySpark, NumPy, etc.). • Strong experience with AWS services—specifically S3, Redshift, Glue (Athena a plus). • Solid understanding of applied statistics. • Hands-on experience More ❯

availability and integrity. Essential: • 3+ years of experience in data engineering, data architecture, or similar roles. • Expert proficiency in Python, including popular data libraries (Pandas, PySpark, NumPy, etc.). • Strong experience with AWS services—specifically S3, Redshift, Glue (Athena a plus). • Solid understanding of applied statistics. • Hands-on experience More ❯

availability and integrity. Essential: • 3+ years of experience in data engineering, data architecture, or similar roles. • Expert proficiency in Python, including popular data libraries (Pandas, PySpark, NumPy, etc.). • Strong experience with AWS services—specifically S3, Redshift, Glue (Athena a plus). • Solid understanding of applied statistics. • Hands-on experience More ❯

availability and integrity. Essential: • 3+ years of experience in data engineering, data architecture, or similar roles. • Expert proficiency in Python, including popular data libraries (Pandas, PySpark, NumPy, etc.). • Strong experience with AWS services—specifically S3, Redshift, Glue (Athena a plus). • Solid understanding of applied statistics. • Hands-on experience More ❯

availability and integrity. Essential: • 3+ years of experience in data engineering, data architecture, or similar roles. • Expert proficiency in Python, including popular data libraries (Pandas, PySpark, NumPy, etc.). • Strong experience with AWS services—specifically S3, Redshift, Glue (Athena a plus). • Solid understanding of applied statistics. • Hands-on experience More ❯

availability and integrity. Essential: • 3+ years of experience in data engineering, data architecture, or similar roles. • Expert proficiency in Python, including popular data libraries (Pandas, PySpark, NumPy, etc.). • Strong experience with AWS services—specifically S3, Redshift, Glue (Athena a plus). • Solid understanding of applied statistics. • Hands-on experience More ❯

availability and integrity. Essential: • 3+ years of experience in data engineering, data architecture, or similar roles. • Expert proficiency in Python, including popular data libraries (Pandas, PySpark, NumPy, etc.). • Strong experience with AWS services—specifically S3, Redshift, Glue (Athena a plus). • Solid understanding of applied statistics. • Hands-on experience More ❯

StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data More ❯

StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data More ❯

StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data More ❯

StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data More ❯

StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data More ❯

StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data More ❯

StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data More ❯

or CloudFormation) Solid understanding of testing frameworks and version control systems (e.g. Git) Nice to Have: Experience with data engineering tools such as Airflow, Pandas, or Spark Exposure to serverless architectures using AWS Lambda Familiarity with monitoring and logging tools (e.g. CloudWatch, Prometheus) Previous experience working in regulated or high More ❯

or CloudFormation) Solid understanding of testing frameworks and version control systems (e.g. Git) Nice to Have: Experience with data engineering tools such as Airflow, Pandas, or Spark Exposure to serverless architectures using AWS Lambda Familiarity with monitoring and logging tools (e.g. CloudWatch, Prometheus) Previous experience working in regulated or high More ❯

or CloudFormation) Solid understanding of testing frameworks and version control systems (e.g. Git) Nice to Have: Experience with data engineering tools such as Airflow, Pandas, or Spark Exposure to serverless architectures using AWS Lambda Familiarity with monitoring and logging tools (e.g. CloudWatch, Prometheus) Previous experience working in regulated or high More ❯