Data Scientist
Virtual Connect Solutions · Eastern England, UK ·
- Category
- Data science
Data Scientist at Virtual Connect Solutions partnering with clients to turn business problems into AI-driven solutions: building, deploying, and monitoring ML, deep learning, and Generative AI (LLM/RAG) models using Python, SQL, and cloud platforms, while communicating insights to technical and business stakeholders.
- Partner with clients and stakeholders to translate business challenges into analytical and AI-driven solutions.
- Perform exploratory data analysis (EDA) to identify trends, insights, and opportunities.
- Design, build, train, and evaluate machine learning, deep learning, and Generative AI models.
- Develop and optimize Retrieval-Augmented Generation (RAG) pipelines and LLM-based applications.
- Define model evaluation frameworks, conduct A/B testing, and monitor model performance and drift.
- Deploy data science solutions through APIs, batch pipelines, and cloud-based platforms.
- Optimize model inference performance, scalability, latency, and cost.
- Build accurate, explainable, and responsible AI solutions while minimizing bias and ensuring reliability.
- Collaborate with data engineers, software engineers, product managers, and designers to integrate solutions into production systems.
- Present insights and recommendations to technical and business stakeholders in a clear and actionable manner.
- Contribute to internal innovation initiatives, research projects, and thought leadership activities.
Required Qualifications
- Bachelor's, Master's, or PhD in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related discipline.
- Strong programming skills in Python and SQL.
- Hands-on experience with machine learning frameworks and the Python data science ecosystem.
- Experience developing predictive, classification, optimization, and statistical models using structured and unstructured data.
- Understanding of Generative AI, LLMs, prompt engineering, RAG architectures, and model evaluation techniques.
- Experience with model deployment, monitoring, testing, and productionization.
- Strong problem-solving and analytical thinking skills.
- Excellent communication and stakeholder management abilities.
Preferred Skills
- Experience with big data technologies such as PySpark, Hadoop, Hive, or similar frameworks.
- Familiarity with Databricks, Airflow, Kedro, Dask, RAPIDS, or comparable data engineering tools.
- Knowledge of cloud platforms including AWS, Azure, or GCP.
- Experience with containerization and orchestration tools such as Docker and Kubernetes.
- Exposure to software engineering best practices and MLOps.
- Experience deploying AI and analytics solutions in enterprise environments.
- Consulting or client-facing project experience is highly desirable.