About us:

Autone is an AI-powered inventory optimisation platform helping retailers transform their business through data-driven decision-making. Founded by Adil Bouhdadi and Harry Glucksmann-Cheslaw, who previously built Alexander McQueen’s intelligence platform, Autone is today trusted by more than 50 global brands to reduce inventory waste, increase sales, and enhance efficiency.

A career with autone is an opportunity to work with some of the best and brightest minds, disrupting the retail industry for a better future. We favour in-office work with employees coming in 3-4 days a week to our Vauxhall office. We’re obviously flexible and considerate about it, but if you prefer working from home on a full-time basis this might not be the best gig for you.

And now to you!

We’re looking for a passionate & optimistic senior data scientist to join our growing product team. As a data scientist, you’ll work closely with our CEO, CTO and stellar data team to develop and improve our statistical models with a focus on demand forecasting and inventory optimisation. You will have ownership over developing and deploying cutting-edge statistical / ML models, as well as shaping our codebase.

Your responsibilities:

• Collaborate with the product team to understand requirements and provide technical solution
• Implement, monitor and deploy advanced statistical / ML models to solve our customers demand planning and inventory optimisation needs.
• Shape our future tech-stack and tooling to enhance ML/AI capabilities
• Review code from colleagues
• Improve data science processes, visibility and explainability perhaps by introducing new data tools or technologies
  
  

What we are looking for:

• A minimum of 2:1 degree in a STEM subject (preferably Computer Science) or comparable experience
• 5-8 years of experience as a data scientist / machine learning engineer, preferably with some experience in an early stage startup! Domain knowledge in retail/commerce is also a plus
• Experience with time series problems, predictive algorithms, and machine learning approaches to forecasting and/or experience with optimization algorithms, including loss functions, constrained-optimization, Bayesian models, etc.
• Experience with tooling for deploying, monitoring, analysing performance and iterating on models
• You relish the thought of taking a large role in an early stage startup and steering its Data Science / ML direction.
• Advanced knowledge of Python with strong experience with common libraries such as scikit-learn, Tensorflow, Pandas, PyTorch, and/or statistical models.
• Strong SQL knowledge
• Experience with AWS is a plus
• Experience with a tool like MLFlow is a plus
• Someone who enjoys working in-office
  
  

The tech stack you’ll be working with

• Python
• SQL (Postgres)
• Tensorflow / PyTorch / sci-kit learn / pandas
• Docker
• Dagster
• AWS Sagemaker, EKS, EMR, Lambda, Athena
• ClickHouse
• Spark
• We pride ourselves on being technologically agnostic, so while this is the current state of our tech-stack, we’re always open to new ideas and technologies that can improve the way we do things. Proficiency in Dagster and listed AWS technologies not necessarily required but experience in similar analogous data tools would be e.g. Airflow, ECS etc.
  
  

Why you’re going to love joining the team

• Ability to have a huge impact within an early stage company, working directly with the founders
• Being hands on with an agile and modern-tech stack with freedom to employ new technologies to achieve goals
• Meritocratic driven career growth
• Fortnightly team events - games nights, sporting events or even just a trip to our local pub
• A competitive compensation package (including stock options)
  
  

The interview process

• CV Screen (45mins) - A chat around your previous work experience as well as an introduction to autone and a chance to find out what we do here
• Data Science Brainstorm (1hr) - work with some potential future colleagues to ideate around a series of challenging data science problems relevant to autone
• Meet a Co-Founder (30mins) - a chance for you to learn more about the company and our grand ambitions