6 of 6 Permanent scikit-learn Jobs in Plymouth

and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms – demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. Terraform). More ❯

generation (RAG) systems Knowledge of optimization techniques for reducing LLM latency and computational costs Software Engineering & Architecture Strong Python development skills (pandas, numpy, scikit-learn, PyTorch/TensorFlow) Experience with web frameworks (Streamlit, FastAPI, or Flask) and frontend technologies Database design and optimization (PostgreSQL preferred) for large protein More ❯

background in Computer Science, Artificial Intelligence, Data Science, Mathematics, or related fields Familiarity with Python programming and libraries such as TensorFlow, PyTorch, or scikit-learn is a plus Basic understanding of machine learning concepts and algorithms Interest or experience in data preprocessing, model training, and evaluation Strong problem More ❯

forecasting models using modern ML techniques. Deep understanding of time series analysis, statistics, and forecasting methodologies. Production-level Python programming skills (Pandas, Polars, Scikit-learn, NumPy, SciPy). Comfort working in a collaborative, cross-functional environment. Please note, this role cannot offer sponsorship at this stage More ❯

translate business questions into analytical frameworks and interpret results for non-technical stakeholders. Strong proficiency in Python, SQL, and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch). Experience with model operationalization using tools like Docker, Kubernetes, MLflow, or SageMaker. Marketing KPIs knowledge: CTR, conversion rate, MQL to … Excellent problem-solving skills, business acumen, and the ability to translate complex models into actionable insights for non-technical stakeholders. Tools/Frameworks : Scikit-learn, XGBoost, LightGBM, StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python … Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses (e.g., Snowflake, Databricks, Redshift, BigQuery) Familiarity with data pipelines and More ❯

Key Skills Required: Comprehensive understanding of the full machine learning lifecycle, from development to production. Experience deploying machine learning models using frameworks like Scikit-learn, TensorFlow, or PyTorch. Proficiency in Python and adherence to software engineering best practices. Strong technical expertise in cloud architecture, security, and deployment, with More ❯