Principal Data Science Consultant with Marketing Expertise (Basé à London)

Jobleads
Greater London
2 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Science Consultant with Marketing Expertise

Principal Data Scientist - Marketing

Mid-Level/Principal Data Scientist

Senior Client Engagement Manager – Data Science

Principal Data Science Consultant - Financial Services Expertise

Recruitment Consultant (Progression to Director)

As one of the world's leading digital transformation service providers, we are looking to expand our Data Practice across Europe to meet increasing client demand for our Data Science and AI services. We are seeking a highly skilled and experiencedData Science Consultantto join our dynamic team.


The ideal candidate will have a strong background in data science, analytics, IT consulting, and experience in marketing-focused projects, preferably within a marketing agency or similar environment. As a Data Science Consultant, you will work closely with clients to understand their business challenges, design and implement data-driven solutions, and provide actionable insights that drive business value. Your ability to bring expertise in marketing analytics and marketing-related data science use cases will be a critical asset.


Responsibilities

  • Support clients with the definition and implementation of their AI strategy, with a particular focus on marketing and customer insights.
  • Implement and oversee AI governance frameworks, focusing on regulatory compliance, ethical AI principles, and ensuring business value from AI investments.
  • Ideate, design, and implement AI-enabled marketing solutions such as customer segmentation models, recommendation systems, and campaign optimization algorithms.
  • Lead the process of taking AI/ML models from development to production, ensuring robust MLOps practices.
  • Monitor and manage model performance, including addressing issues related to explainability, data drift, and model drift.
  • Develop and implement marketing measurement frameworks, including marketing mix modeling (MMM) and multi-touch attribution (MTA).
  • Collaborate with marketing teams to leverage data for customer journey mapping, lifetime value prediction, and churn analysis.
  • Engage with senior executives, effectively communicating AI opportunities, risks, and strategies in accessible terms, particularly in marketing and customer engagement contexts.
  • Collaborate with legal teams to navigate AI regulatory risks, particularly in the context of the EU AI regulatory framework.
  • Maintain up-to-date knowledge of industry trends, emerging technologies, and regulatory changes impacting AI/ML, particularly in the marketing domain.
  • Support pre-sales activities including client presentations, demos, and RFP/RFI responses.

Requirements

  • Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Physics, Marketing Analytics, or a related field. Ph.D. is a plus.
  • 2+ years of experience in data science, analytics, or related roles within the IT consulting, marketing agency, or digital marketing space.
  • Strong communication skills, comfortable presenting to senior business leaders, particularly in marketing and customer engagement contexts.
  • Deep understanding of LLMs, their strengths and limitations, and their application in marketing personalization, chatbots, and content generation.
  • Proven experience in marketing-focused data science projects, such as customer segmentation, campaign performance analytics, and predictive modeling for marketing strategies.
  • Familiarity with AI/ML tools and platforms commonly used in marketing data science, such as Tableau, Power BI, Google Analytics, HubSpot, or Salesforce Marketing Cloud.
  • Strong understanding of ML Ops principles and experience in model deployment and management, particularly for marketing use cases.
  • Ability to articulate complex AI risks and strategies to non-technical stakeholders, including senior executives in marketing and sales.
  • Expertise in identifying and mitigating bias in AI/ML models, especially in consumer-facing applications.
  • Proficiency in Python and familiarity with AI/ML tools and platforms such as Azure, AWS, GCP, Databricks, MLFlow, Airflow, Plotly Dash, and Streamlit.
  • Experience with advertising platforms (e.g., Facebook Ads, Google Ads) and programmatic ad-buying strategies is a plus.
  • Knowledge of marketing metrics, customer lifetime value (CLV), and return on investment (ROI) modeling.

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.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.