Data Scientist

Role: Data Scientist Location: Remote Contract: 06 MonthsRate: 450-500 GBP Per Day – Inside IR35 MAXThe Data Scientist will be responsible for Data Collection & Cleaning; Analysis and Modeling; Insight and Communication in line with the requirements of the project. Working collaboratively with the existing team to ensure project deadlines are met.The chosen worker will be expected to carry out the belowCollect, clean, and preprocess structured and unstructured data from multiple internal and external sources.Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies.Design and implement data pipelines for model-ready datasets in collaboration with data engineering teams.Apply feature engineering and selection techniques to improve model accuracy and interpretability.Develop and validate machine learning and statistical models for prediction, classification, clustering, or optimization.Apply supervised and unsupervised learning techniques using libraries such as Scikit-learn, TensorFlow, or PyTorch.Implement NLP, time-series forecasting, or optimization algorithms based on project requirements.Evaluate models using appropriate metrics and perform hyperparameter tuning for optimal performance.Convert proof-of-concept models into production-grade pipelines in collaboration with MLOps and engineering teams.Experience in having done the following is desiredTranslate model outcomes into actionable insights through clear storytelling and visualizations.Build dashboards and reports using Power BI, Tableau, or Python-based visualization tools.Communicate findings to both technical and non-technical stakeholders effectively.Partner with business analysts, architects, and domain experts to define use cases and success metrics.Contribute to the enterprise AI roadmap, bringing thought leadership on analytical methodologies.Document methodologies, model logic, and validation results for audit and reproducibility.Participate in Agile ceremonies, sprint planning, and client showcases.Strong proficiency in Python (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch).Solid background in statistics, probability, and mathematical modeling.Hands-on experience with machine learning algorithms, feature engineering, and model evaluation.Familiarity with SQL, data wrangling, and ETL processes.Exposure to cloud-based ML services (Azure ML, AWS SageMaker, GCP Vertex AI).Experience with version control (Git) and containerized deployment (Docker/Kubernetes).