Senior Data Scientist

Autone
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

About Autone

Autone is reimagining the future of retail with cutting-edge AI-driven intelligence, empowering brands to make smart, waste-free decisions that drive growth and efficiency. Trusted by over 50 global brands, we blend advanced AI and deep retail expertise to unlock seamless collaboration between supply chains and human insight. The result? Reduced inventory waste, boosted sales, and precise forecasting that saves money and enhances operations - all while empowering teams to focus on what they do best.

Founded in London in 2021 and backed by industry giants like Y Combinator & General Catalyst, Autone is moving fast to transform retail as we know it. Join us on this journey to a smarter, more agile future.

What We're Looking For

We're on the hunt for a passionateSenior Data Scientistto join our team, bringing expertise in statistical and machine learning models to enhance demand forecasting and inventory optimization. If you're excited about building and deploying models that tackle real-world challenges, we'd love to hear from you.

Core Responsibilities:

  1. Collaborate with our product team to understand requirements and develop technical solutions.
  2. Implement, monitor, and deploy advanced statistical and machine learning models to address customer needs in demand planning and inventory optimization.
  3. Shape and enhance our tech stack, tooling, and processes to optimize ML/AI capabilities.
  4. Conduct code reviews and support ongoing improvements in data science processes, explainability, and visibility.

Tech Stack You'll Be Working With

  1. Languages: Python, SQL (Postgres)
  2. ML/AI Libraries: TensorFlow, PyTorch, scikit-learn, Pandas
  3. Infrastructure: Docker, AWS Sagemaker, EKS, EMR, Lambda, Athena
  4. Orchestration & Data Tools: Dagster (or Airflow), ClickHouse, Spark
    We pride ourselves on being technologically adaptable. While the above is our current tech stack, we're open to new technologies that can improve our workflows. Experience with analogous tools is also valuable.

What You'll Bring to Autone

Must-Haves:

  1. A 2:1 degree in a STEM field (preferably Computer Science) or equivalent experience.
  2. 5-8 years of experience as a Data Scientist or Machine Learning Engineer, ideally with some of that experience in a startup/scaleup.
  3. Strong experience with time series analysis, predictive algorithms, machine learning models for forecasting, and optimization algorithms (such as loss functions, constrained-optimization, and Bayesian models).
  4. Proficiency in Python and key libraries such as scikit-learn, TensorFlow, Pandas, and/or PyTorch.
  5. Strong SQL skills.
  6. Experience with tooling for model deployment, monitoring, and performance analysis.

Nice-to-Haves:

  1. Domain knowledge in retail or e-commerce.
  2. Familiarity with AWS (Sagemaker, EKS, EMR, Lambda, Athena).
  3. Experience with MLFlow or similar model management tools.
  4. Familiarity with Dagster or similar orchestration tools (e.g., Airflow).

What Autone offers you

  1. High Impact: As a Senior Data Scientist at Autone, you'll have a central role in shaping our data science and ML strategy.
  2. Creative Freedom: Significant ownership over model development and deployment, with room to innovate.
  3. Career Growth: A meritocratic, high-growth environment where your career path is yours to steer.
  4. Team Culture: Fortnightly team events - from games to sports outings - and casual pub visits to foster team spirit.

Compensation

£90-£110K + equity, depending on experience

The Interview Process

We value transparency in our hiring process. Here's what you can expect:

  1. Stage 1: CV Screen (45 mins)An introductory chat to explore your experience and provide an overview of Autone.
  2. Stage 2: Data Science Brainstorm (1 hour)Collaborate with potential colleagues on data science challenges relevant to our work at Autone.
  3. Stage 3: Meet a Co-Founder (30 mins)Learn more about our vision and goals directly from one of our founders.

Application Deadline

The deadline to apply is23:59 GMT on Wednesday, 27th November 2024

#J-18808-Ljbffr

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.