puestos de Biotech Scientist en Gran Bretaña

We are seeking a highly skilled and motivated Data Scientist / AI Engineer to join our AI team. In this hybrid role, you will extract insights from complex data sets and develop robust AI models to power intelligent solutions. The ideal candidate will have strong expertise in statistical modeling, machine learning, and software engineering, with a deep understanding of deploying models in production environments.

Key Responsibilities :

• Design, develop, and implement advanced machine learning models to solve business problems.
• Conduct data exploration, cleaning, preprocessing, and feature engineering.
• Build and evaluate predictive models, classification systems, recommendation engines, or NLP / vision solutions.
• Deploy AI / ML models into production using APIs, containers, and scalable cloud platforms.
• Collaborate with cross-functional teams including data engineers, product managers, and domain experts to identify opportunities for AI-driven enhancements.
• Apply best practices in model monitoring, version control, and retraining strategies (MLOps).
• Stay current with the latest research and trends in data science, deep learning, and AI technologies.
• Present insights and model results to both technical and non-technical stakeholders in a clear and actionable manner.

Requirements :

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or a related field.
• 3–6 years of experience in a data science or AI / ML engineering role.
• Strong programming skills in Python (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch).
• Experience working with structured and unstructured datasets.
• Solid understanding of algorithms, probability, statistics, and model evaluation techniques.
• Proficiency in cloud-based ML platforms (AWS SageMaker, Google Vertex AI, Azure ML).
• Familiarity with DevOps / MLOps tools (Docker, MLflow, Airflow, Kubernetes) is a plus.
• Excellent communication and collaboration skills.

Preferred Qualifications :

• Experience with NLP (e.g., BERT, GPT), Computer Vision, or time-series modeling.
• Knowledge of SQL, big data tools (Spark, Hive), and data pipelines.
• Experience with CI / CD for machine learning projects.