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Data Scientist

About Hook

Hook's mission is to empower subscription businesses with machine learning in order to help them improve customer loyalty and revenue generation from existing customers.

We’re a Series A company who have raised $multi-million in funding from some of the top investors in Europe and the US (e.g. Balderton Capital and Lightspeed) and have an enviable list of customers that we’re working with including some of the fastest growing companies in the world. We’re looking for ambitious people that want to be part of our meaningful vision of changing how businesses engage with their customers, and of building one of the world’s leading workplaces for great people along the way.

One of the foundational technical challenges at Hook is to create self-service data and ML pipelines for our customers which surface useful business outputs like churn and upsell prediction.

Responsibilities:

You will wear many hats as a data scientist at Hook. Your day-to-day will include:

  • Deeply understanding product problems for anchoring machine learning solutions

  • Getting embedded in customer implementations and delivering high quality models

  • Assessing the quality and completeness of data from different sources

  • Using different machine learning and statistical techniques and evaluating their performance

  • Clearly communicating with internal and external stakeholders to explain model results and underlying data

  • Collaborating in a team with technical and non-technical colleagues

  • Owning significant parts of our data science processes and proposing and leading new improvements

  • Continuously monitoring model performance and ensuring high quality consistent results

There’s a lot going on - you’ll be building a brand new category of SaaS product! The more you want to be involved, the more you will find there is to do and the more impact you will have on our joint success.

Requirements:

We're looking for an ambitious self-starter who wants to be part of an early-stage company full of amazing talent and with a global vision.

  • Strong product mindset with a keen interest in using data to solve customer problems

  • Experience working with different machine learning and statistical modeling techniques

  • Experience working with data and machine learning products in production

  • Excellent communication skills, with the ability to effectively collaborate and convey ideas to diverse stakeholders

  • Experience working with Python and different data and ML libraries

  • Experience working with SQL and relational databases

  • Desirable: Experience/interest in working with LLMs in production

Benefits

🏖️ 27 days holiday (option to buy and sell holiday) + bank holidays

📈 Generous stock options

🏥 Private health insurance so you can get the best care you need

🧘 Flexible monthly wellness allowance that you can use monthly on things like gyms, yoga, mental health & healthy food

📚 Annual learning & development budget for every employee

🏢 Flexible working - we’ll all come together three days a week but otherwise you can work from home or come into our awesome office in Liverpool Street

💻 The best equipment for everyone to work with

Our founder and team are incredibly passionate about creating a company culture that is diverse, inclusive, productive and enjoyable for all our colleagues. As part of a growing start-up team, we expect a lot from you and will work hard to make sure you have everything you need to be at your best.

Average salary estimate

$70000 / YEARLY (est.)
min
max
$60000K
$80000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Data Scientist, Hook

At Hook, we're on a mission to empower subscription businesses through the innovative use of machine learning, helping them boost customer loyalty and maximize revenue. As a Data Scientist in our vibrant London office, you'll join a Series A company that has successfully raised multi-million funding from prestigious investors like Balderton Capital and Lightspeed. Your hands-on role will involve diving into complex product problems to create impactful machine learning solutions and building self-service data pipelines that deliver insights like churn and upsell predictions. You’ll collaborate closely with both technical and non-technical team members as you engage with customer implementations, ensuring the models you develop are of the highest quality. Strong communication skills are key, as you'll need to articulate model results to various stakeholders clearly. We value ambition and a proactive mindset, and we're excited to bring on self-starters who want to have a significant impact in an early-stage company packed with exceptional talent. By joining us, you'll not only work on groundbreaking solutions but also experience the perks of 27 days of holiday, generous stock options, private health insurance, and a flexible work environment. Come help us build something incredible at Hook!

Frequently Asked Questions (FAQs) for Data Scientist Role at Hook
What are the main responsibilities of a Data Scientist at Hook in London?

As a Data Scientist at Hook, your main responsibilities will include understanding product challenges, delivering high-quality machine learning models, and collaborating with stakeholders to communicate insights effectively. You will also be involved in continuous evaluation and monitoring of model performance while proposing improvements to our data science processes.

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What qualifications are required to become a Data Scientist at Hook?

To qualify for the Data Scientist position at Hook, candidates should possess strong experience with machine learning and statistical modeling techniques, proficiency in Python and SQL, and excellent communication skills. Additionally, an interest in working with LLMs in production can be advantageous.

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What team environment can a Data Scientist expect at Hook?

At Hook, Data Scientists can look forward to a collaborative environment where they work closely with both technical and non-technical colleagues. You're encouraged to take ownership and have the autonomy to propose and lead improvements in our data science processes, all while being supported by a passionate and inclusive team culture.

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How does Hook support the professional development of Data Scientists?

Hook supports the professional growth of its Data Scientists through an annual learning and development budget. This allows team members to invest in their skills and knowledge, enhancing their expertise in machine learning and data science.

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What benefits does Hook offer to its Data Scientists?

Hook offers a robust benefits package for its Data Scientists, including 27 days of holiday, generous stock options, private health insurance, a flexible wellness allowance, and a dynamic work environment that fosters collaboration and innovation.

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Common Interview Questions for Data Scientist
Can you describe your experience with machine learning techniques relevant to the Data Scientist role at Hook?

When answering this question, be specific about the machine learning models you have developed and their applications in real-world scenarios. Highlight any successful projects, the impact they had, and how you used metrics to measure performance.

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How do you prioritize tasks when working on multiple data science projects at the same time?

It's vital to demonstrate your organizational skills here. Discuss how you assess project urgency, potential impact, and require collaboration. Provide examples of tools or methodologies you use, such as Agile or Kanban to manage your tasks effectively.

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Explain a time when you had to communicate complex data science concepts to a non-technical audience.

Share a specific example and focus on how you broke down the concepts into relatable terms. Explain the strategies you employed to ensure clarity, such as the use of visual aids or analogies, and the feedback you received.

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What approaches do you use to evaluate the performance of a machine learning model?

Discuss different metrics you've used, like accuracy, precision, recall, or F1-score, depending on the project's objectives. Explain how you interpret these metrics to make data-driven decisions regarding model adjustments or improvements.

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Describe your experience with Python and SQL in the context of data science.

Provide specific examples of the projects in which you've utilized Python for data manipulation and model building, as well as how you've used SQL for managing relational databases. Highlight any libraries or frameworks you're familiar with.

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How do you keep up-to-date with the latest developments in data science and machine learning?

Emphasize your commitment to continuous learning. Mention reputable sources such as academic papers, online courses, and industry conferences. Sharing specific examples of how you’ve recently implemented new knowledge in projects will make your answer stronger.

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What is your experience with deploying machine learning models into production?

Mention any frameworks or tools you've used for deployment, such as Docker or cloud services. Detail the process you followed from model development to deployment, including any challenges faced and solutions implemented.

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How would you handle incomplete or messy data in a project?

Discuss your data cleaning techniques and the importance of data preprocessing. Mention any specific strategies you use, such as imputation methods or filtering out irrelevant data, detailing how you ensure data quality for your analyses.

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Can you give an example of how you utilized data to solve a real business problem?

Select a project where your data-driven insights led to a measurable business outcome. Explain the problem, the analysis conducted, the solutions proposed, and how the implementation benefited the company.

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What kind of team collaboration experience do you have in previous data science roles?

Detail your experiences working within interdisciplinary teams, emphasizing the importance of collaboration. Share examples where you facilitated discussions between technical and non-technical members to achieve common goals.

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H By Hook
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Full-time, hybrid
DATE POSTED
January 10, 2025

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