6 of 6 Pandas Jobs in the South East

Computer Science, Mathematics, Statistics or another STEM-related discipline. Desirable Skills Experience with cloud platforms such as AWS, Azure or GCP . Experience with Pandas, NumPy and Scikit-learn . Knowledge of data visualisation tools such as Power BI or Tableau. Experience with Git/version control. Exposure to Generative ...

implementing data science processes. Experience in translating customer requirements into technical requirements and development objectives. Python expertise covering standard data science libraries (NumPy, pandas, matplotlib, Scikit-learn, PyTorch, etc...), web frameworks (Flask or FastAPI) and visualisation (Dash, Plotly, & Streamlit). Proficient in SQL. Working with unstructured data Building customer ...

implementing data science processes. · Experience in translating customer requirements into technical requirements and development objectives. · Python expertise covering standard data science libraries (NumPy, pandas, matplotlib, Scikit-learn, PyTorch, etc...), web frameworks (Flask or FastAPI) and visualisation (Dash, Plotly, & Streamlit). · Proficient in SQL. · Working with unstructured data · Building customer ...

champion best practices in MLOps. What You Bring ·Proven experience building and iterating on ML models in a commercial setting. ·Expert-level Python skills (pandas, NumPy, scikit-learn, etc.) and strong statistical grounding. ·Experience working within cloud or modern data environments (Microsoft Fabric, Azure, or Databricks). ·Clear, impactful communication ...

Experience in managing data and implementing data science processes. Experience in translating customer requirements intotechnical requirements and development objectives. Pythonexpertisecovering standard data sciencelibraries (NumPy, pandas, matplotlib, Scikit-learn,PyTorch, etc),web frameworks (Flask or FastAPI) and visualisation(Dash,Plotly,&Streamlit). Proficient inSQL. Working with unstructured data Building customer ...

sound validation, and an instinct for leakage, bias and overfitting in messy real-world data. Strong ML fundamentals across classical ML and deep learning (pandas, scikit-learn, XGBoost or LightGBM, PyTorch), and the judgement to tell when a language model is the wrong tool. A deep understanding of how modern ...