Principal Data Science & ML Engineering Consultant Manchester, UK

Responsibilities

• Collaborate with clients to define their data science and ML strategies, ensuring alignment with business objectives and technical feasibility
• Lead the design, development, deployment and maintenance of ML models, emphasizing MLOps best practices for scalability and reliability
• Design and implement data pipelines to process, transform and prepare data for ML workflows
• Monitor, evaluate and improve model performance, addressing issues like data drift, model drift and latency in production environments
• Build CI/CD pipelines for seamless integration of ML models into production systems
• Work with cross-functional teams, including data engineers, software developers and business stakeholders, to ensure the successful implementation of ML solutions
• Implement AI governance frameworks, ensuring compliance with ethical practices and industry regulations
• Stay at the forefront of industry trends, emerging ML technologies and innovative tools to continually enhance service offerings
• Translate complex ML concepts into actionable insights and technical roadmaps for stakeholders at various levels
• Contribute to client-facing activities, including presentations, workshops and responses to RFPs/RFIs

Requirements

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Software Engineering or related fields. A Ph.D. is an advantage
• Extensive experience in data science, ML engineering or related roles. Experience in leading teams on projects in not required but would be valued
• Deep understanding of ML lifecycle management, including feature engineering, model selection, hyperparameter tuning, model validation, model evaluation and deployment for inference
• Hands-on expertise in deploying ML models at scale in production environments (via platforms such as AWS SageMaker or Azure ML), and optimising models for efficient inference using formats like ONNX and TensorRT
• Proficiency in Python and ML/engineering frameworks such as PyTorch, TensorFlow (including Keras), Hugging Face (Transformers, Datasets) and scikit-learn, etc
• Experience with MLOps tools, including MLFlow, workflow orchestrators (Airflow, Metaflow, Perfect or similar), and containerisation (Docker)
• Strong knowledge of cloud platforms like Azure, AWS or GCP for deploying and managing ML models
• Familiarity with data engineering tools and practices, e.g., distributed computing (e.g., Spark, Ray), cloud-based data platforms (e.g., Databricks) and database management (e.g., SQL)
• Strong communication skills, capability to present technical concepts to technical and non-technical stakeholders
• Experience in developing AI applications using large language models (LLMs) and Retrieval-Augmented Generation (RAG) systems (via LangChain, LlamaIndex or custom API-driven approaches)

We offer

• EPAM Employee Stock Purchase Plan (ESPP)
• Protection benefits including life assurance, income protection and critical illness cover
• Private medical insurance and dental care
• Employee Assistance Program
• Competitive group pension plan
• Cyclescheme, Techscheme and season ticket loans
• Various perks such as free Wednesday lunch in-office, on-site massages and regular social events
• Learning and development opportunities including in-house training and coaching, professional certifications, over 22,000 courses on LinkedIn Learning Solutions and much more
• If otherwise eligible, participation in the discretionary annual bonus program
• If otherwise eligible and hired into a qualifying level, participation in the discretionary Long-Term Incentive (LTI) Program
• *All benefits and perks are subject to certain eligibility requirements