1 to 25 of 133 Pandas Jobs

quantitative modelling bridging the gap between data and lived football judgement Essential Requirements Technical Skills Advanced proficiency in Python and the data science stack (pandas/polars, scikit-learn, NumPy, matplotlib/plotly) Strong SQL skills ability to design schemas, write complex queries, and manage relational data across large datasets ...

learning methods and large language models. Extensive experience with ML/DL toolkits and libraries (e.g., Transformers, Hugging Face, TensorFlow, PyTorch, NumPy, scikit-learn, pandas). Demonstrated leadership in proposing and delivering AI/ML and GenAI solutions; ability to drive technical direction and influence stakeholders. Experience designing experiments, training ...

developers and quantitative teams on new analytics capabilities Participate in debugging, testing, and production support activities Technology Environment Python Databricks/Apache Spark SQL Pandas/NumPy Git REST APIs Cloud technologies (Azure or AWS) Numerix (or equivalent pricing/risk platform) Jupyter/notebook-based analytics workflows Requirements ...

value What We’re Looking For Essential experience Significant experience building, training and deploying machine learning models Strong Python skills with experience using NumPy, Pandas, SciPy and modern ML frameworks such as PyTorch or TensorFlow Experience working with large-scale unstructured data Experience building and deploying production-ready NLP systems ...

such as Tableau or Power BI. Published work, patents or conference presentations related to AI or data science in finance. Technical Skills Programming: Python (pandas, scikit‐learn, TensorFlow/PyTorch), R, SQL, Jupyter notebooks and version control (Git). Machine Learning: regression, classification, clustering, tree‐based models, neural networks, MLOps ...

experience in Data Engineering or a similar technical discipline Strong Python skills with production-level engineering experience Experience with tools/frameworks such as pandas, Polars, DBT Strong understanding of data warehousing, dimensional modelling and medallion architecture Hands-on experience with cloud platforms: AWS, Azure or GCP Experience with orchestration ...

MLOps: Establish CI/CD pipelines and manage model deployment using Kubernetes/OpenShift and MLOps principles. Technical Requirements Core Programming: Expert-level Python (Pandas, NumPy, scikit-learn, PyTorch/TensorFlow, FastAPI, LangChain, LlamaIndex). Generative AI: Deep experience with LLMs (Gemini, OpenAI, Claude, Llama) and evaluating model safety/ ...

stability and performance over time. Required Skills and Qualifications Technical Expertise (The Core) Programming: Expert proficiency in Python and its core data science libraries: Pandas, NumPy, and SciPy . Machine Learning: Deep, practical experience with popular ML frameworks and libraries: scikit-learn, TensorFlow, or PyTorch . Statistics & Math: Strong foundation ...

optimization. Experience with distributed systems, parallel computing, and high-performance processing of large datasets. Strong experience in data pipelines, working with tools such as Pandas, NumPy, and SQL/NoSQL databases. Proven experience working in fast-paced environments, ideally within trading, financial services, or high-frequency environments. Proficiency in developing ...

entity resolution. GenAI: Experience with LLM APIs and Context Engineering (constructing prompts, managing context windows, evaluating behaviour). Engineering & Stack: Python: Expert level (Pandas, NumPy, Scikit-Learn). Data Engineering: Strong SQL skills and experience building data pipelines. Experience: Education: Degree in a quantitative field (Statistics, Mathematics, Computer Science, etc. ...

time‐series databases, visualization tools). Published research, patents, or conference presentations in AI or data science related to finance. Technical Skills Programming: Python (pandas, scikit‐learn, TensorFlow/PyTorch), R, SQL, notebooks, Git. Machine Learning: regression, classification, clustering, tree‐based models, neural networks; MLOps, model deployment, automated testing. Data ...

clearly Required Qualifications Currently pursuing or recently completed a degree in Data Science, Computer Science, Statistics, Mathematics , or related field Strong foundation in Python (Pandas, NumPy, Scikit-learn) Understanding of machine learning concepts (regression, classification, clustering) Experience with data visualization tools (Matplotlib, Seaborn, or similar) Familiarity with SQL and working ...

Python and familiarity with common AI/ML libraries (scikit-learn, PyTorch, HuggingFace, OpenAI API). Bonus Points Experience with data engineering tools (SQL, pandas, dbt, Airflow). Exposure to LLMs or generative AI in a practical context. GitHub profile demonstrating collaborative projects and consistent contributions. Who we are: Here ...

scikit-image, PIL) and classical CV algorithms (edge detection, thresholding, color spaces). Data Analytics Fluency: Experience working with standard data manipulation libraries (NumPy, pandas, scikit-learn, SciPy). Communication: Advanced English proficiency to collaborate effectively in a global environment. Bonus Points (Nice to Have) Hands-on experience with Vision ...

looking for a talented ML Ops Engineer to join our growing teams. Key Responsibilities Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools ...

Experience Required Senior-level experience in data science or a quantitative field Proficient programming skills (Python preferred); familiarity with core data science libraries (NumPy, Pandas, Scikit-Learn) Experience with deep learning tools (e.g. PyTorch, TensorFlow, or similar) Strong mathematical/statistical background Familiarity with a wide range of data science ...

/service development experience. Knowledge of cloud platforms (AWS, GCP, Azure), distributed systems, and CI/CD pipelines. Strong data manipulation skills (SQL, pandas) and experience evaluating AI system outputs. Ability to work with ambiguity and optimise non-deterministic systems while balancing latency, cost, and quality trade-offs. Desirable Skills ...

efficiency. Contributing to a culture of knowledge-sharing and continuous improvement. Key Skills & Experience Strong Python skills and familiarity with ML libraries such as Pandas, NumPy, and scikit-learn. Experience with frameworks such as TensorFlow, Keras, or PyTorch. Exposure to gradient boosting tools such as XGBoost, LightGBM, or CatBoost. Experience ...

models that perform reliably in complex operational environments. What We’re Looking For Strong experience in Python and SQL, including libraries such as Pandas, NumPy and scikit-learn. Experience working with deep learning frameworks such as PyTorch or TensorFlow. A solid grounding in statistics and probability, with strong mathematical foundations ...

Data Engineering, Computer Science, or similar Experience working in commodities, energy trading, or financial markets environments Advanced Python skills, including extensive use of Pandas Experience with analytical or time-series databases (e.g. Redshift, ClickHouse) Hands-on experience with Docker and containerised workloads Experience with Git and modern DevOps practices Practical ...

efficiency. Contributing to a culture of knowledge-sharing and continuous improvement. Key Skills & Experience Strong Python skills and familiarity with ML libraries such as Pandas, NumPy, and scikit-learn. Experience with frameworks such as TensorFlow, Keras, or PyTorch. Exposure to gradient boosting tools such as XGBoost, LightGBM, or CatBoost. Experience ...

Python skills by building a car price prediction app using Python and Streamlit. Step 6 – Python for Data Learn essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualisation. Step 7 – AI Sentiment Analysis Project Work with Hugging Face to build a sentiment analysis classifier using real ...

Python skills by building a car price prediction app using Python and Streamlit. Step 6 – Python for Data Learn essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualisation. Step 7 – AI Sentiment Analysis Project Work with Hugging Face to build a sentiment analysis classifier using real ...

Python skills by building a car price prediction app using Python and Streamlit. Step 6 – Python for Data Learn essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualisation. Step 7 – AI Sentiment Analysis Project Work with Hugging Face to build a sentiment analysis classifier using real ...

Python skills by building a car price prediction app using Python and Streamlit. Step 6 – Python for Data Learn essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualisation. Step 7 – AI Sentiment Analysis Project Work with Hugging Face to build a sentiment analysis classifier using real ...