Senior Data Science Consultant – Econometrics specialist

Epam
London
11 months ago
Create job alert

Description

ABOUT THE ROLE



Are you passionate about Data Science? Do you enjoy working with both technical and business stakeholders to translate vision and designs into sustainable, customer-focused solutions?

Can you communicate efficiently and influence quicker deliveries? If yes, we have new position for a Senior Data Science Consultant. The successful candidate will be a key player in driving the development and implementation of advanced pricing and marketing optimization models. The role involves leveraging deep expertise in Bayesian statistics, causal inference and econometric methods, as well as proficiency in Python, to deliver impactful insights and solutions in the CPG (Consumer Packaged Goods) domain.

Responsibilities

Design and build sophisticated pricing and marketing optimization models using Bayesian, causal inference and econometric approaches Develop optimization models and employ Monte Carlo simulations for robust analysis Lead A/B testing initiatives for accurate measurement and validation of models Analyze large datasets to identify trends, patterns and actionable insights Collaborate with cross-functional teams to understand business needs and provide data-driven solutions Proficiently use Python for model development and ensure models are production-ready Manage the end-to-end process of taking models to production, ensuring scalability and reliability Utilize Azure, Databricks, MLFlow, Airflow and Plotly Dash for efficient model deployment and visualization Apply domain knowledge in CPG pricing and promotion optimization to enhance model accuracy and relevance Work closely with other data scientists, engineers and business stakeholders Mentor junior team members and contribute to the team's knowledge sharing

Requirements

Masters degree or higher in a quantitative field (e.g., Computer Science, Statistics, Physics, Mathematics) Minimum of 5 years of experience in a data science role with a focus on pricing and marketing optimization Proven expertise in Bayesian, causal inference and econometric methods Strong proficiency in Python and experience in taking models to production Experience with cloud computing platforms, preferably Azure and tools such as Databricks, MLFlow Airflow and Plotly Dash

Nice to have

PhD in a relevant field Prior experience in the CPG industry, specifically in pricing and promotion optimization

Our Benefits Include

A competitive group pension plan and protection benefits including life assurance, income protection and critical illness cover Private medical insurance and dental care Cyclescheme, Techscheme and season ticket loans Employee assistance program Great learning and development opportunities, including in-house professional training, career advisory and coaching, sponsored professional certifications, well-being programs, LinkedIn Learning Solutions and much more EPAM Employee Stock Purchase Plan (ESPP) Various perks such as gym discounts, free Wednesday lunch in-office, on-site massages and regular social events Certain benefits and perks may be subject to eligibility requirements and may be available only after you have passed your probationary period

About EPAM

EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential

Related Jobs

View all jobs

Senior Economist/Data Scientist

Senior Data Science Consultant, Customer Data & Technology

Senior Data Engineering Consultant

Senior Recruitment Consultant - AI & Data Science - Manchester

Senior Credit Risk Modeller- Data Scientist

Graduate Actuarial Analyst (Machine Learning)

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.

Jobs

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.

Jobs

Machine Learning Jobs in the Public Sector: Opportunities Across GDS, NHS, MOD, and More

Machine learning (ML) has rapidly moved from academic research labs to the heart of industrial and governmental operations. Its ability to uncover patterns, predict outcomes, and automate complex tasks has revolutionised industries ranging from finance to retail. Now, the public sector—encompassing government departments, healthcare systems, and defence agencies—has become an increasingly fertile ground for machine learning jobs. Why? Because government bodies oversee vast datasets, manage critical services for millions of citizens, and must operate efficiently under tight resource constraints. From using ML algorithms to improve patient outcomes in the NHS, to enhancing cybersecurity within the Ministry of Defence (MOD), there’s a growing demand for skilled ML professionals in UK public sector roles. If you’re passionate about harnessing data-driven insights to solve large-scale problems and contribute to societal well-being, machine learning jobs in the public sector offer an unparalleled blend of challenge and impact. In this article, we’ll explore the key reasons behind the public sector’s investment in ML, highlight the leading organisations, outline common job roles, and provide practical guidance on securing a machine learning position that helps shape the future of government services.

Jobs

Contract vs Permanent Machine Learning Jobs: Which Pays Better in 2025?

Machine learning (ML) has swiftly become one of the most transformative forces in the UK technology landscape. From conversational AI and autonomous vehicles to fraud detection and personalised recommendations, ML algorithms are reshaping how organisations operate and how consumers experience products and services. In response, job opportunities in machine learning—including roles in data science, MLOps, natural language processing (NLP), computer vision, and more—have risen dramatically. Yet, as the demand for ML expertise booms, professionals face a pivotal choice about how they want to work. Some choose day‑rate contracting, leveraging short-term projects for potentially higher immediate pay. Others embrace fixed-term contract (FTC) roles for mid-range stability, or permanent positions for comprehensive benefits and a well-defined career path. In this article, we will explore these different employment models, highlighting the pros and cons of each, offering sample take‑home pay scenarios, and providing insights into which path might pay better in 2025. Whether you’re a new graduate with a machine learning degree or an experienced practitioner pivoting into an ML-heavy role, understanding these options is key to making informed career decisions.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.