At a Glance
- Tasks: Lead complex data science projects and mentor teams to drive innovation.
- Company: Dynamic retail consultancy transforming businesses with AI expertise.
- Benefits: Unlimited annual leave, private healthcare, flexible working, and coaching support.
- Other info: Diverse team culture fostering creativity and collaboration.
- Why this job: Shape the future of retail with cutting-edge technology and impactful solutions.
- Qualifications: Expertise in machine learning, project management, and strong leadership skills.
The predicted salary is between 60000 - 80000 £ per year.
About the team
Our Retail and Consumer experts are dedicated to helping clients in an industry which is being transformed by new technologies and evolving consumer expectations. Leveraging over a decade of experience in Applied AI, we combine exceptional technical and delivery expertise to empower businesses to adapt and thrive.
About the role
As a Lead Data Scientist, you'll take on a pivotal, entrepreneurial role, functioning as the technical expert who thrives on complexity and commercial impact. You will be responsible for setting the technical direction and ensuring the high-quality, scalable delivery of our most challenging, high-impact projects. This position combines deep expertise in machine learning with strategic oversight to define project roadmaps, manage technical risk, and architect reliable solutions. Your focus will be on driving innovation, mentoring cross‑functional teams, and actively shaping both our technical standards and long‑term customer relationships.
What you'll be doing:
- Setting the technical direction for complex, business-critical projects and expertly balancing trade‑offs between speed, innovation, and reliability.
- Designing and implementing reliable, production‑grade technical solutions, ensuring comprehensive documentation of architectures and specifications.
- Defining project problems, developing clear roadmaps, and overseeing end‑to‑end delivery across multi‑disciplinary workstreams.
- Leading technical scoping and feasibility studies for high‑value sales opportunities and strategic customer engagements.
- Managing relationships and communications with demanding clients, fostering trust and aligning technical solutions with shared long‑term commercial goals.
- Driving the adoption of best practices, shared resources, and robust technical processes across the wider Data Science craft.
- Mentoring and developing other data scientists and team members, actively contributing to the growth and technical excellence of the organisation.
Who we're looking for:
- You bring depth of expertise in at least one machine learning domain and strong technical breadth across the entire data science landscape.
- You are a skilled technical leader, proficient in mentoring individuals, managing teams (including other managers), and rolling out impactful tools and workflows.
- You have proven project management expertise, capable of dividing complex, ill‑defined problems into actionable, clearly defined workstreams with timelines you can defend.
- You are adept at managing ill‑defined, high‑risk tasks, consistently delivering innovative and practical outcomes under commercial pressure.
- You possess strong customer leadership skills, able to act as a trusted technical advisor and drive long‑term strategic relationships with demanding clients.
- You excel at cross‑functional collaboration, effectively aligning technical strategy with Engineering, Commercial (BD), and Infrastructure teams.
- You have experience extending technical oversight to business unit‑level initiatives, using your vision to influence and contribute to organisational success.
Our Interview Process
- Talent Team Screen (30 minutes)
- Introduction to the team (30 minutes)
- Take Home Technical Assessment
- Technical Interview (90 minutes)
- Commercial Interview (60 minutes)
Our Recruitment Ethos
We aim to grow the best team - not the most similar one. We know that diversity of individuals fosters diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We’re united by a deep intellectual curiosity and desire to use our abilities for measurable positive impact. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions and sexual orientations.
Some of our standout benefits:
- Unlimited Annual Leave Policy
- Private healthcare and dental
- Enhanced parental leave
- Family-Friendly Flexibility & Flexible working
- Sanctus Coaching
- Hybrid Working
Lead Data Scientist - Retail in London employer: Faculty
As a Lead Data Scientist in our Retail and Consumer team, you'll join a forward-thinking company that values innovation and technical excellence. We offer a dynamic work culture that promotes diversity, collaboration, and personal growth, alongside standout benefits like unlimited annual leave and private healthcare. Our commitment to mentoring and developing talent ensures that you will thrive in your role while making a meaningful impact on our clients' success.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Scientist - Retail in London
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Lead Data Scientist - Retail in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Faculty, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Faculty. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Faculty
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Faculty!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
