Senior Data Science Consultant – Econometrics specialist

Epam
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Science Consultant: Bayesian Pricing & Marketing Optimization

Senior Data Scientist & Consultant: Drive Real Impact

Senior Data Scientist SME & AI Architect

Senior Consultant - Data Scientist

Snowflake Data Engineer | Senior Consultant

Senior Data Engineer & AI Analytics Consultant

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

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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

Industry Insights

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

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.