12 of 12 SciPy Jobs in the UK

toxicity modelling Automated hedging and inventory optimisation Numerical optimisation or machine learning applied to pricing or trading Python quantitative libraries such as NumPy, SciPy and Pandas Low-latency or high-throughput trading systems Quant Dev Quant Developer Quantitative Developer Java Java 21 Java 22 Java21 Java22 J21 J22 Institutional ...

calling, agentic workflows), including evaluation and safety patterns. Expertise with machine learning frameworks and data science packages (e.g., PyTorch, TensorFlow, Scikit‐Learn, NumPy, Pandas, SciPy, statsmodels). Experience deploying to AWS (e.g., SageMaker, Bedrock) and operating production workloads with attention to cost, performance, security, and scaling. Experience integrating human ...

Quantitative Finance, Mathematics, Physics, Engineering, Computer Science, or a related STEM discipline Strong understanding of data fundamentals and their python implementations (numpy, polars, scipy) Strong knowledge of AI/ML techniques with experience delivering related work into production Curious to explore how AI can improve how we build, deliver ...

experience Preferred Qualifications Experience using managed ML/AI solutions Experience with modeling tools such as R, scikit‐learn, Spark MLLib, MxNet, TensorFlow, NumPy, SciPy etc. Experience with large scale distributed systems such as Hadoop, Spark etc. Amazon is an equal opportunities employer. We believe passionately that employing a diverse ...

Exposure to recommendation algorithms, NLP, or People Analytics is a major plus. Python Mastery: Fluent in Python and specialized machine learning libraries (scikit-learn, SciPy, statsmodels). Experience with NLP frameworks (spaCy, HuggingFace, or text embeddings) is highly valued. Statistical Rigor: Solid foundation in applied statistics, including clustering, predictive modeling ...

Exposure to recommendation algorithms, NLP, or People Analytics is a major plus. Python Mastery: Fluent in Python and specialized machine learning libraries (scikit-learn, SciPy, statsmodels). Experience with NLP frameworks (spaCy, HuggingFace, or text embeddings) is highly valued. Statistical Rigor: Solid foundation in applied statistics, including clustering, predictive modeling ...

STEM discipline Proven experience delivering data science, machine learning or AI solutions in a commercial environment Strong programming capability in Python (Pandas, NumPy, SciPy or similar) Solid grounding in statistics, probability and data analysis AI & LLM Experience Hands-on experience with large language models, including: RAG pipelines Fine-tuning approaches ...

machine learning techniques such as regularised regression, clustering or tree-based ensembles (and experience applying them with packages like Pandas, sci-kit-learn, SciPy) is highly beneficial Experience processing big data, ideally in a Hadoop/Spark environment, would be beneficial Proven experience implementing CI/CD and DevOps practices ...

structured, or unstructured data, especially large-scale text or content datasets. Familiarity with common data science and machine learning tools such as Pandas, NumPy, SciPy, Scikit-learn, PyTorch, TensorFlow, or Matplotlib. The ability to translate complex and ambiguous requirements into practical, measurable, data-driven solutions, with strong analytical thinking, problem ...

industry setting (excluding post‐doc positions), with exposure to: building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications; developing models for bespoke problem settings that involve high‐dimensional data (spatiotemporal, geometric, physical); iterating on network ...

experience conducting research in machine learning, evidenced by publications or prior research roles. Experience in programming with Python-based scientific libraries (e.g., NumPy, SciPy, JAX, or TensorFlow). Experience with language models (LLMs), transformers, diffusion models, text diffusion, or distributed training. Experience with machine learning, mathematics, and statistics (e.g., linear ...

building machine learning models and pipelines in Python, using common libraries and frameworks (PyTorch/CUDA, ideally with exposure to JAX, NumPy/SciPy), especially including deep learning applications; developing models for bespoke problem settings that involve high‐dimensional data (spatiotemporal, geometric, physical); iterating on network architectures and model structure ...