14 of 14 Polars Jobs in the City of London

type hints and understanding the limitations of the language) and in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data pipelines for More ❯

type hints and understanding the limitations of the language) and in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data pipelines for More ❯

a seasoned mid-level Software Engineer with up to 6+ years of hands-on Python experience. Familiarity with AWS Serverless and related technologies would be advantageous. Strengths in Pandas, Polars, Pytest, Postgres, and CI/CD is essential. If you're interested in Agile methodologies, and have a curiosity about DDD and Event-Driven architectures, you'd fit right in. More ❯

a seasoned mid-level Software Engineer with up to 6+ years of hands-on Python experience. Familiarity with AWS Serverless and related technologies would be advantageous. Strengths in Pandas, Polars, Pytest, Postgres, and CI/CD is essential. If you're interested in Agile methodologies, and have a curiosity about DDD and Event-Driven architectures, you'd fit right in. More ❯

this team, and to work on data and trading infrastructure and a scalable data lake (AWS), which will ingest data sets from market data vendors. Stack: Python, Pandas/Polars, AWS, ETL This is a great opportunity for someone inside or outside of finance, to join a high performing team and build data systems that yield profit for the firm. More ❯

this team, and to work on data and trading infrastructure and a scalable data lake (AWS), which will ingest data sets from market data vendors. Stack: Python, Pandas/Polars, AWS, ETL This is a great opportunity for someone inside or outside of finance, to join a high performing team and build data systems that yield profit for the firm. More ❯

evaluation, and statistical inference is highly valued. Python expertise: Skilled in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data pipelines for More ❯

evaluation, and statistical inference is highly valued. Python expertise: Skilled in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data pipelines for More ❯

object-oriented programming, List comprehensions, generators, Error handling, iii. Working with virtual environments and package management (pip, venv) Data Manipulation & Analysis (Pandas & NumPy) i. Key libraries: pandas, numpy, (optional: polars) ii. Key skills: Data cleaning and preprocessing, Handling missing values, grouping, merging, pivoting, aggregations, and SQL Software Engineering Best Practices i. Key practices: Version control with Git. Writing modular, reusable More ❯

classes, and object-oriented programming, List comprehensions, generators, Error handling, Working with virtual environments and package management (pip, venv) Data Manipulation & Analysis (Pandas & NumPy) Key libraries: pandas, numpy, (optional: polars) Key skills: Data cleaning and preprocessing, Handling missing values, grouping, merging, pivoting, aggregations, and SQL Software Engineering Best Practices Key practices: Version control with Git. Writing modular, reusable code. Unit More ❯

object-oriented programming, List comprehensions, generators, Error handling, iii. Working with virtual environments and package management (pip, venv) Data Manipulation & Analysis (Pandas & NumPy) i. Key libraries: pandas, numpy, (optional: polars) ii. Key skills: Data cleaning and preprocessing, Handling missing values, grouping, merging, pivoting, aggregations, and SQL Software Engineering Best Practices i. Key practices: Version control with Git. Writing modular, reusable More ❯

classes, and object-oriented programming, List comprehensions, generators, Error handling, Working with virtual environments and package management (pip, venv) Data Manipulation & Analysis (Pandas & NumPy) Key libraries: pandas, numpy, (optional: polars) Key skills: Data cleaning and preprocessing, Handling missing values, grouping, merging, pivoting, aggregations, and SQL Software Engineering Best Practices Key practices: Version control with Git. Writing modular, reusable code. Unit More ❯

Communicate findings effectively to both technical and non-technical stakeholders, including coaches, scouts, analysts, and traders. Skills & Attributes Strong background in Python, with experience in data manipulation (Pandas/Polars) and statistical modelling. Industry experience in a data science role focussing on sports analytics. Knowledge of advanced football metrics (possession value models highly desirable). A deep passion for football More ❯

Communicate findings effectively to both technical and non-technical stakeholders, including coaches, scouts, analysts, and traders. Skills & Attributes Strong background in Python, with experience in data manipulation (Pandas/Polars) and statistical modelling. Industry experience in a data science role focussing on sports analytics. Knowledge of advanced football metrics (possession value models highly desirable). A deep passion for football More ❯