Senior Data Scientist

Autone
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

SENIOR DATA SCIENTIST - Computer Vision / Generative AI HYBRID

Senior Data Scientist (GenAI)

Senior Data Scientist (MLOps)

Principal/Senior Data Scientist

Senior Data Engineer

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

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Job-Hunting During Economic Uncertainty: Machine Learning Edition

Machine learning (ML) has firmly established itself as a crucial part of modern technology, powering everything from personalised recommendations and fraud detection to advanced robotics and predictive maintenance. Both start-ups and multinational corporations depend on machine learning engineers and data experts to gain a competitive edge via data-driven insights and automation. However, even this high-demand sector can experience a downturn when broader economic forces—such as global recessions, wavering investor confidence, or unforeseen financial events—lead to more selective hiring, stricter budgets, and lengthier recruitment cycles. For ML professionals, the result can be fewer available positions, more rivals applying for each role, or narrower project scopes. Nevertheless, the paradox is that organisations still require skilled ML practitioners to optimise operations, explore new revenue channels, and cope with fast-changing market conditions. This guide aims to help you adjust your job-hunting tactics to these challenges, so you can still secure a fulfilling position despite uncertain economic headwinds. We will cover: How market volatility influences machine learning recruitment and your subsequent steps. Effective strategies to distinguish yourself when the field becomes more discerning. Ways to showcase your technical and interpersonal skills with tangible business impact. Methods for maintaining morale and momentum throughout potentially protracted hiring processes. How www.machinelearningjobs.co.uk can direct you towards the right opportunities in machine learning. By sharpening your professional profile, aligning your abilities with in-demand areas, and engaging with a focused ML community, you can position yourself for success—even in challenging financial conditions.

How to Achieve Work-Life Balance in Machine Learning Jobs: Realistic Strategies and Mental Health Tips

Machine Learning (ML) has become a cornerstone of modern innovation, powering everything from personalised recommendation engines and chatbots to autonomous vehicles and advanced data analytics. With numerous industries integrating ML into their core operations, the demand for skilled professionals—such as ML engineers, research scientists, and data strategists—continues to surge. High salaries, cutting-edge projects, and rapid professional growth attract talent in droves, creating a vibrant yet intensely competitive sector. But the dynamism of this field can cut both ways. Along with fulfilling opportunities comes the pressure of tight deadlines, complex problem-solving, continuous learning curves, and high-stakes project deliverables. It’s a setting where many professionals ask themselves, “Is true work-life balance even possible?” When new algorithms emerge daily and stakeholder expectations soar, the line between healthy dedication and perpetual overwork can become alarmingly thin. This comprehensive guide aims to shed light on how to achieve a healthy work-life balance in Machine Learning roles. We’ll discuss the distinctive pressures ML professionals face, realistic approaches to managing workloads, strategies for safeguarding mental health, and how boundary-setting can be the difference between sustained career growth and burnout. Whether you’re just getting started or have been at the forefront of ML for years, these insights will empower you to excel without sacrificing your well-being.

Transitioning from Academia to the Machine Learning Industry: How PhDs and Researchers Can Thrive in Commercial ML Settings

Machine learning (ML) has rapidly evolved from an academic discipline into a cornerstone of commercial innovation. From personalising online content to accelerating drug discovery, machine learning technologies permeate nearly every sector, creating exciting career avenues for talented researchers. If you’re a PhD or academic scientist thinking about leaping into this dynamic field, you’re not alone. Companies are eager to recruit professionals with a strong foundation in algorithms, statistical methods, and domain-specific knowledge to build the intelligent products of tomorrow. This article explores the essential steps academics can take to transition into industry roles in machine learning. We’ll discuss the differences between academic and commercial research, the skill sets most in demand, and how to optimise your CV and interview strategy. You’ll also find tips on networking, developing a commercial mindset, and navigating common challenges as you pivot your career from the halls of academia to the ML-driven tech sector.