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For Companies At a Glance
- Tasks: Build machine learning models and design algorithms to enhance retail strategies.
- Company: Retail Insight transforms data into actionable strategies for retailers and CPGs.
- Benefits: Enjoy hybrid working, generous PTO, and comprehensive health benefits.
- Why this job: Join a dynamic team focused on innovation and impactful analytics in retail.
- Qualifications: STEM background with programming experience in Python or R; familiarity with machine learning.
- Other info: Flexible work environment with a commitment to equal opportunities and personal growth.
The predicted salary is between 66000 - 77000 £ per year.
Turn Data into Action with Retail Insight.
At Retail Insight (RI), we transform data into actionable strategies, empowering retailers and CPGs to make smarter decisions. Our cutting-edge algorithms and innovative retail execution products are trusted by many of the world’s leading companies to improve sales, profitability, and operational efficiency.
From tackling out-of-stocks and poor in-store execution to reducing waste, markdowns, and shrink, RI helps businesses unlock performance drivers through advanced analytics. Find out more about our journey here.
About the role:
Our Decision Science teams focus on delivering immediate positive impact to our clients using the latest machine learning and data analytics techniques. We partner with international clients to develop and implement our products. We are now looking for a talented Decision Scientist, responsible for building Machine Learning models to support our products, including:
What you will do…
- Design algorithms using relevant statistical and machine learning techniques.
- Define the quantitative elements of projects for internal R&D and external clients, primarily success criteria.
- Contribute to the design, implementation, and testing of our products.
- Communicate with stakeholders at various levels in both technical and non-technical language.
- Maintain a continuous improvement mindset, with attention to detail and pride in your work.
- Write high-quality, well-documented code.
- Have proven experience working on analytics projects within a STEM background.
- Adopt a pragmatic approach to deadlines, focusing on deliverables.
- Possess practical programming experience, ideally using Python, R, etc.
- Apply knowledge of Machine Learning, Data Science, and Statistical techniques.
- Present results and proposals to cross-functional teams.
- Be familiar with cloud computing tools, ideally Azure.
Nice to haves…
- Experience in a retail background is beneficial.
- Keen on continuous technical development, data analytics trends, and tools.
Some of our extras…
We believe in flexibility, growth, and taking care of our people—both in and out of the office.
Compensation: The salary range for this role is $95,000 – $110,000 plus great benefits.
Hybrid & Flexible Working: Work from anywhere with 2 days in-office per week, plus summer hours and extra days off for life’s big moments.
Generous Time Off: 26 days PTO (increasing with service), paid volunteer days, and full support for wellbeing, including mental health resources.
Career & Learning: Access to top-tier learning platforms, career progression plans, and support for home workspace setup.
Health & Financial Benefits: Comprehensive medical, dental, and vision coverage, plus a 6% matched retirement plan.
Personal Perks: Annual phone reimbursement, onsite gym access, and customizable benefits via our online platform.
Plus much more!
Our interview process typically involves 3 stages:
Initial Call with the Recruiter
Meet the Hiring Manager
Skills Assessment with the team
Our average process takes around 3-4 weeks but we will work around your availability. You will have the chance to speak with our recruiter at various points, but if you have any questions, please contact us.
Be your authentic self – Retail Insight is committed to promoting equal opportunities in employment. All employees and job applicants will receive equal treatment. We seek to create an environment where everyone feels respected, supported, and encouraged to contribute their best work.
About Us
We’re a unique blend of retail expertise from extensive industry experience, mathematical talent that builds and maintains sophisticated algorithms, and engineering skill that handles vast data volumes.
We combine leading-edge technology with innovative thinking to help you tackle operational challenges and maximize retail potential.
We focus on factors that drive sales and profit, minimize waste and loss, and increase operational efficiency. The result: dynamic solutions that provide actionable insights and rapid time to value.
Our values underpin everything we do at Retail Insight. They drive us, define us, and shape our decision-making. They are also why the world’s largest retailers and CPGs find working with us so easy.
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Decision Scientist II employer: Retail Insight Limited
Contact Detail:
Retail Insight Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Decision Scientist II
✨Tip Number 1
Familiarise yourself with the latest machine learning techniques and algorithms relevant to retail analytics. Being able to discuss specific models you've worked on or studied can really impress during interviews.
✨Tip Number 2
Showcase your ability to communicate complex data insights in a clear manner. Practice explaining your past projects to friends or family who may not have a technical background, as this will help you articulate your thoughts during interviews.
✨Tip Number 3
Research Retail Insight's current products and case studies. Understanding their offerings and how they apply machine learning to solve real-world problems will allow you to tailor your discussions and demonstrate genuine interest.
✨Tip Number 4
Network with professionals in the retail analytics field. Attend industry meetups or webinars to connect with others and gain insights into the skills and experiences that are currently in demand.
We think you need these skills to ace Decision Scientist II
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data science, machine learning, and programming. Emphasise any projects where you've designed algorithms or worked with statistical techniques, as these are key aspects of the role.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data analytics and how your skills align with Retail Insight's mission. Mention specific experiences that demonstrate your ability to communicate complex ideas to both technical and non-technical stakeholders.
Showcase Your Technical Skills: Include a section in your application that lists your technical skills, particularly in Python, R, and cloud computing tools like Azure. Provide examples of how you've applied these skills in previous roles or projects.
Prepare for the Interview Process: Familiarise yourself with common interview questions related to data science and machine learning. Be ready to discuss your past projects in detail, especially those that involved algorithm design and implementation, as well as your approach to problem-solving.
How to prepare for a job interview at Retail Insight Limited
✨Understand the Role
Make sure you have a solid grasp of what a Decision Scientist II does. Familiarise yourself with machine learning techniques, data analytics, and how they apply to retail insights. This will help you answer questions confidently and demonstrate your knowledge.
✨Showcase Your Technical Skills
Be prepared to discuss your programming experience, particularly in Python or R. Bring examples of your previous work on analytics projects, and be ready to explain the algorithms you've designed and implemented.
✨Communicate Effectively
Practice explaining complex technical concepts in simple terms. You'll need to communicate with both technical and non-technical stakeholders, so being able to adapt your language is crucial.
✨Demonstrate Continuous Improvement Mindset
Retail Insight values a continuous improvement mindset. Be ready to discuss how you've approached challenges in the past, what you've learned from them, and how you stay updated on data analytics trends and tools.