Principal Data Science & ML Engineering Consultant

EPAM; City Of London; On-site; GBP 90,000 - 130,000; Full time

Job summary

A leading technology consultancy in London seeks a Principal Data Science & ML Engineering Consultant to deliver scalable ML solutions and bridge data science with software engineering. You will lead model deployment, ensure governance, and optimize performance across various industries. Strong expertise in ML tools and communication skills are essential for this impactful role.

Benefits

Employee Stock Purchase Plan

Private medical insurance

Various in-office perks such as free lunch

Qualifications

• Extensive experience in data science, ML engineering or related roles.
• Deep understanding of ML lifecycle management.
• Hands-on expertise in deploying ML models at scale.

Responsibilities

• Collaborate with clients to define data science and ML strategies.
• Lead the design, development, deployment and maintenance of ML models.
• Monitor and improve model performance in production environments.

Skills

Data Science

Machine Learning

MLOps

Python

Cloud Platforms

Education

Bachelor's or Master's degree in Data Science, Statistics, Computer Science or related fields

Ph.D. (advantage)

Tools

AWS SageMaker

Azure ML

Docker

PyTorch

TensorFlow

Job description

Overview

As a global leader in digital transformation, we are expanding our Data Practice across Europe to address growing client demand for advanced Data Science and ML engineering services. We are seeking a talented and experienced Principal Data Science & ML Engineering Consultant to join our dynamic team. This role emphasizes building scalable, production-ready ML solutions, optimizing model performance and driving technical innovation across diverse industries.

In this position, you will bridge the gap between data science and software engineering, delivering robust data-driven solutions that empower clients to solve real-world challenges and unlock measurable value.

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

Qualifications

• Bachelor's or Master019s 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)

Benefits

• 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