13 of 13 Polars Jobs

place of a degree Experience with the following Data Science technologies Various machine learning libraries (i.e. Apache Spark, Scikit-learn, XGBoost, etc.) Data manipulation and pipeline libraries (i.e. Pandas, Polars, matplotlib, Plotly, numpy, scipy, etc.) Data science environments (e.g. Jupyter Notebook, Data Bricks, or Amazon Sage Maker) Experience implementing the data science process, developing experiments, reporting and explaining results Experience More ❯

of experience developing AI/ML applications, data science, or algorithm development Experience with Python and data science/machine learning libraries (e.g. PyTorch, TensorFlow, Keras, OpenCV, NumPy, Pandas, Polars, scikit-learn, etc.) Experience with one or more of the following: Applying unsupervised and/or supervised machine learning techniques Applying and/or developing algorithms based in statistical analysis More ❯

PostgreSQL, NoSQL, Vector Databases, Graph Databases etc. ETL/ELT Concepts Data warehouse concepts SQL Competency in data exploration, analytics, and feature engineering. (Python Specific) Pandas/NumPy/Polars/PySpark Plotly/Matplotlib (some form of data visualization) Data encoding/normalizing/regularizing/etc. Understanding of Deep learning concepts and architectures like CNNs, RNNs, LSTM, and More ❯

produce analytical or visual products Knowledge of data formats such as JSON, Parquet, Avro, or CSV Ability to work with Dataframes and SQL utilizing Spark, PySpark, Python Pandas, or Polars Ability to report findings and research clearly and concisely for clients Secret clearance Bachelor's degree in a Computer Science or Computer Engineering field and 2+ years of experience engineering More ❯

a career switcher or even have some commercial experience already. Whatever the case, you will be passionate about coding and excited to learn more. Stack includes: Python, dataclasses, Pandas, Polars, Pytest, Asyncio, Postgres, and AWS services such as ECS, Lambda and SQS - so experience with these technologies or alternatives would be helpful, but as long as you have a good More ❯

a career switcher or even have some commercial experience already. Whatever the case, you will be passionate about coding and excited to learn more. Stack includes: Python, dataclasses, Pandas, Polars, Pytest, Asyncio, Postgres, and AWS services such as ECS, Lambda and SQS - so experience with these technologies or alternatives would be helpful, but as long as you have a good More ❯

a career switcher or even have some commercial experience already. Whatever the case, you will be passionate about coding and excited to learn more. Stack includes: Python, dataclasses, Pandas, Polars, Pytest, Asyncio, Postgres, and AWS services such as ECS, Lambda and SQS - so experience with these technologies or alternatives would be helpful, but as long as you have a good More ❯

a career switcher or even have some commercial experience already. Whatever the case, you will be passionate about coding and excited to learn more. Stack includes: Python, dataclasses, Pandas, Polars, Pytest, Asyncio, Postgres, and AWS services such as ECS, Lambda and SQS - so experience with these technologies or alternatives would be helpful, but as long as you have a good More ❯

or related field Minimum 6 years of relevant experience as a Data Engineer/Scientist Experience developing data pipelines and normalizing data with canonical Python packages (e.g. NumPy, Pandas, Polars) Active TS/SCI U.S. Government Security Clearance with a recent Full-Scope Polygraph (FSP) Preferred Qualifications: M.S. or PhD in data science, AI/ML, computer science, or related 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 ❯

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 or personal projects related to football data. Knowledge of advanced football metrics (possession value models highly 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 ❯

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 ❯