Sam Watkins
About
Sam Watkins is from Nuneaton, England, United Kingdom. Sam is currently Senior Data Scientist at Lead Forensics, located in Nuneaton, England, United Kingdom. Sam also works as Data Scientist at Solera Claims Solutions, a job Sam has held since Jan 2019. Another title Sam currently holds is Consumer Insight Data Scientist at Energy Systems Catapult.
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Sam Watkins's current jobs
Lead on the design, development, and deployment of all data science/machine learning/AI services. This includes the deployment of services into live production via CI/CD. Languages: Python, SQL, R Algorithms and Approaches: Supervised machine learning (XGBoost), unsupervised machine learning (kmeans, multinomial/gaussian mixtures), deep learning (word2vec, transfer learning [mobilenet]), transformers/LLMs (BERT, BART, HuggingFace), LLM fine tuning, AI, Agentic AI (LangChain/LangGraph) productionisation, web scraping (selenium). And, Bayesian inference (Bayes A/B testing). Cloud platforms: GCP (cloud build, cloud run, cloud tasks, cloud scheduler), Azure (azure SQL, azure functions, azure task scheduler), Snowflake (data loading, management and querying). Containerisation: Docker Repositories: GitHub Documentation: Confluence
Leading the development of the business unit's machine learning and artificial intelligence deployments. Technologies: Python, R, SQL, Tensorflow, Keras. Algorithms: - Supervised Learning (Neural Networks [MobileNet, Transfer Learning, Mask RCNN] & Gradient Boosting [XGBoost, LightGBM, CatBoost]) - Unsupervised Learning (Gaussian and Multinomial Mixture Modelling, K-means). -Natural Language Processing (NLTK, Word2Vec) Deployment: Flask, Docker, FastAPI Cloud Computing: AWS EC2, Azure Container Repo, Azure App Service, Google Cloud Platform (Vision API, Cloud Build, Cloud Run, API Gateway, AI Platform).
Lead on consumer data analytics and insight using a variety of models, tools, and platforms. Tools/Languages: Python (matplotlib, scikit learn, pandas), R (Dplyr, ggplot(2), bnlearn, choicemodelR, nnet, cluster, PRcomp, mixtools, etc.), SPSS (linear models, mix models, PCA). Approaches: Segmentation, Conjoint, Exploratory Analysis, Principal Components Analysis, Mixed Modelling Model Types: Bayes/Probabilistic, Parametric, Non-Parametric Databases: MongoDB, SQL, AWS, Cassandra Data Types: Sensor Data, Survey Data, Demographics, private/sensitive data, openly available data.
Provide data science expertise across the business, to external organisations and government departments. Tasks vary from hands on data science (data attainment, data management, data transformation and cleaning, data analysis), to findings and insight dissemination (to all levels of the business pyramid), and data science strategy development (both to private business and government departments). A key aspect of my role is to promote a holistic view of data science: Maths & Statistics, Programming & Databases, Communication & Visualization, Domain/Business Knowledge & Soft Skills. Tools and Platforms currently implemented: R, Python, iPython Notebook, Hadoop, SQL, AWS, Azure, SPSS, Excel, XML.