[Hiring] Lead Data Science Consultant @Centric Software

Role Description

At Centric, we help some of the world's most recognized fashion, beauty, and home brands decide what to stock, what to promote, and what to charge, and we help them do it with AI. Our AI-based planning and pricing solution runs at more than 50 customers across over ten countries.

As Lead Data Science Consultant, you run the team behind that work. Four data scientists today, ten by the end of next year. You decide who joins, how the team works, and how we turn a customer's messy historical data into forecasts and pricing decisions they are willing to act on.

The role has two halves that have to fit in one person:

• One half faces the customer: you sit with retailers and brands, work out how they really make their decisions, and turn that into something a model can answer.
• The other half is technical: you keep the methods honest, know when a gradient boosting model is the right answer and when it is not, and are curious about what Generative AI can take over in work that used to be manual.

If you want to build a team and stay close to the mathematics at the same time, this is that role.

What You'll Do

• Build and lead the team
• Grow the data science consulting team from four people to ten over the next year, and set the bar for who gets hired.
• Develop the consultants in your team with regular feedback, clear growth paths, and technical standards that hold across projects.
• Decide how the team works: how projects are staffed, how knowledge moves between them, and how quality gets checked before anything reaches a customer.
• Own the customer-facing analytical work
• Act as the senior technical counterpart for our customers, from the first onboarding workshop through to the solution running in production.
• Turn business processes and commercial questions into concrete modelling approaches, then explain the result in language a planner or a buyer can use.
• Take the hardest projects yourself when that is what the situation needs.
• Push the methods forward
• Own the forecasting, pricing, and inventory models our customers depend on: time series, statistical modelling, supervised learning, and optimization.
• Decide where Generative AI earns its place. The first targets are customer onboarding and the mapping and linking of unfamiliar data structures, which is where most of the manual effort sits today.
• Keep a clear line between what is proven and what is still an experiment, and make sure the team knows which is which.
• Work across the organization
• Partner with Product, Engineering, and Implementation on what the solution needs next.
• Carry the evidence from customer projects back into the product roadmap.
• Design and analyze A/B tests that settle open questions with data instead of opinion.

Qualifications

• 8+ years of hands-on data science work, a good part of it spent in front of customers and not only in front of code.
• Experience leading people or projects, whether as a team lead, a chapter lead, or a sustained project lead others relied on.
• A strong grounding in classical machine learning and statistics. You can explain why a method works and where it breaks.
• Excellent SQL and solid Python (Pandas, NumPy, Statsmodels or equivalent).
• The instinct to run a project: scoping it, estimating effort, keeping a timeline honest, and flagging early when something will not land.
• Excellent communication in English, including a feel for how much detail a given audience actually wants.
• A degree in mathematics, statistics, computer science, physics, economics, or a related field.

Requirements

• Experience in retail, fashion, or consumer products.
• Practical work with LLMs beyond prompting, for example retrieval, evaluation, or agentic workflows in production.
• Big data environments such as PySpark.
• Familiarity with pricing or behavioral price effects.
• Additional languages beyond English.

Benefits

• You build the team. Six hires over the next year, and you set the standard they are measured against.
• Remote, and we mean it. Our customers sit in more than ten countries, so we do not move people around for the sake of it. Expect to travel once or twice a year, usually when a customer happens to be within a couple of hours of where you live.
• Work with consequences. Our models set prices and stock levels at companies you have heard of, and the effect shows up in their numbers.
• A field that is still open. We are early in applying Generative AI to onboarding and data mapping, and you would decide what that becomes here.
• Competitive salary and benefits, and a global team with deep expertise in planning, pricing, and inventory optimization.