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Hybrid Data Scientist & ML/AI Engineer

The role

We are looking for a talented Data Scientist / Machine Learning Engineer to join our dynamic data team at Mumsnet. You will play a key role in building data-driven products and leveraging AI/LLM technologies to enhance our platform and user experience. Your work will have a direct impact on our 9 million users, driving innovation through machine learning and data analytics. 

You will be responsible for designing, developing, and deploying machine learning models, as well as integrating AI-driven solutions via APIs. Strong communication skills are essential, as you will collaborate with teams across product, engineering, and commercial to translate their requirements into actual data products. We have successfully built our very own AI platform, MumsGPT, which is a combination of AI-agents that bring together and analyse all Mumsnet’s qualitative and quantitative data in a super efficient and user friendly way. In this role, you will be responsible for maintaining and improving the platform as well as building new ML/AI driven data products.

This is a fantastic opportunity to be part of an exciting, growing team that is shaping the future of Mumsnet with cutting-edge data science and AI solutions.

What you’ll be doing

Every day in the Mumsnet data team is different. We work with web traffic, textual data, and large datasets to develop new data products and AI-powered features. Your responsibilities will include:

  • Machine Learning & AI Development:
    • Designing, training, and deploying machine learning models for predictive analytics, personalization, and automation, with a particular focus on NLP applications.
    • Leveraging AI/LLMs (e.g., GPT, Gemini, LLaMA) for content moderation, entity extraction, analysing and summarising huge amounts of text data, and other business needs.
  • Data Product Development:
    • Building and maintaining scalable data products, pipelines, and REST APIs.
    • Implementing ML models in production, ensuring performance, monitoring, and scalability.
  • Data Engineering & Analytics:
    • Working with ingestion and processing of structured and unstructured data from multiple sources.
    • Collaborating with team members to optimize data infrastructure and ETL workflows.
  • Stakeholder Collaboration & Communication:
    • Partnering with the product team to implement data-driven features.
    • Presenting insights and recommendations to both technical and non-technical stakeholders.
    • Documenting and sharing best practices for AI/ML model development.

Why work for us?

We focus on steady, sustainable growth, putting purpose (to make parent’s lives easier) before profit. We expect great performance, agility and collaboration in every role; cross-team working with talented, clever people is the best part of life at Mumsnet. Most importantly we’re looking for candidates with a growth mindset - we know everyone makes mistakes, the important thing is to learn from them and to share your learnings.

We embraced flexible and home working a long time before COVID, and we care much more about outputs than hours on the clock. We don't only want to hire the best people: we want to retain them. If you need some flexibility, let us know and we’ll do our best.

We’re committed to diversity and quality (see our policy ) and we think we’re second to none in the all-round support we offer to parents and carers at work (see our parental leave policies ). Working for Mumsnet means never having to pretend you don’t have a family or other commitments, and we promise never to keep you away from a school appointment. We are also proud to be a London Living Wage employer and we have never used unpaid interns.

We have a healthy line-up of sandwich lunches, knowledge sessions, book clubs, yoga sessions, monthly socials and sports teams, as well as an unhealthy line-up of staff parties at Kentish Town’s excellent pubs and restaurants, and McDonald’s deliveries for the mornings after.

WFH

We’ve continued to embrace flexibility and remote working to the max. You’ll only be required to be in the office two days a week (although you can go in more often if you wish). We’re flexible with hours and happy to accommodate personal commitments like picking the kids up from school. We’re more interested in output than presenteeism, so if you need to move things around, we can probably work it out.

How we'll support you

  • Weekly or bi-weekly 1-1 with line manager to review progress, agree roles, provide support;
  • Formal 6-month appraisal system;
  • Buddy and Mentor Programmes;
  • LinkedIn online learning - bi-weekly DEAL(Drop Everything And Learn) sessions, training to be agreed with your line manager;
  • Ongoing on the job training from your line manager.

This role sits within our data team and reports into the Chief Data Officer and is based at our offices in Kentish Town. This role is envisaged as full-time but we are happy to consider flexible options for the right candidate.

If all of this sounds good to you, please apply with your CV and a covering letter that demonstrates some of the qualities we’re looking for.

  • Technical Skills:
    • Strong proficiency in Python and relevant frameworks/libraries (NumPy, Pandas, Scikit-Learn, TensorFlow/PyTorch, LangChain, LangGraph, Hugging Face, etc.).
    • Experience working with LLMs and AI models in production environments.
    • Experience building RAG-based applications and vector databases
    • Experience with graph databases like Neo4J or Memgraph
    • Hands-on experience with SQL and cloud platforms (GCP, AWS, or Azure).
    • Experience building APIs for AI/ML models using FastAPI, Flask, or similar frameworks.
    • Proficiency in data visualization tools (Looker, Streamlit, Plotly, matplotlib, etc.).
    • Knowledge of MLOps best practices for deployment, monitoring, and model lifecycle management.
    • Knowledge of general CI/CD best practices
    • Experience with Docker for containerisation and deployment
  • Soft Skills:
    • Strong analytical mindset and problem-solving abilities.
    • Ability to communicate complex data concepts to non-technical audiences.
    • Passion for learning new AI/ML technologies and applying them in real-world applications.
  • Nice to Have:
    • Exposure to A/B testing and experimental design.
    • Familiarity with Reinforcement Learning with Human Feedback (RLHF).

We’re pleased to offer the following benefits, subject to eligibility:

  • Salary range between £45,000-£75,000 depending on expertise and experience
  • 25 Days Holiday
  • Buy More Holiday Scheme
  • Cycle2Work Scheme
  • Employee Assistance Programme
  • Mumsnet Workplace Pension Scheme
  • Electric Vehicle Scheme
  • LinkedIn Learning Subscription with fortnightly “Drop Everything And Learn” time
  • Leisure and Retail perks discounts through the Perkbox platform
  • BUPA Private Medical cover
  • Wellness benefits including daily workouts and meditations via Perkbox
  • Monthly team social events
  • Annual team bonus opportunities .

Want to know more?

Check out  - our guide to what it's like to work at Mumsnet. Read about our mission, our vision, our values and the behaviours we expect of the people who work at Mumsnet.

Mumsnet Glassdoor Company Review
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Mumsnet DE&I Review
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CEO of Mumsnet
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Justine Roberts
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Average salary estimate

$60000 / YEARLY (est.)
min
max
$45000K
$75000K

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 Hybrid Data Scientist & ML/AI Engineer, Mumsnet

At Mumsnet, we're on the lookout for a talented Hybrid Data Scientist & ML/AI Engineer to join our innovative data team. In this thrilling role, you'll be at the forefront of building enlightening data-driven products that enhance the experience of our 9 million users. Your expertise will directly shape the future of our platform, leveraging the power of AI and machine learning technologies. Imagine designing, developing, and deploying machine learning models that drive predictive analytics and personalization! Not to mention, integrating AI-driven solutions via APIs to make our services even more efficient. Strong communication skills are essential because you’ll collaborate constantly with teams across product development, engineering, and commercial to transform ideas into exceptional data products. You will also be responsible for our home-grown AI platform, MumsGPT, which efficiently analyzes qualitative and quantitative data in a user-friendly manner. On any given day, you’ll dive deep into web traffic, textual data, and colossal datasets, all while contributing to developing new AI-powered features. With a growth-focused mindset, Mumsnet prioritizes steady growth and a supportive work environment. Here, flexibility is key—you'll mainly work from home, collaborating remotely with colleagues, but we're happy to see you in the office twice a week if that suits your style! Join us in crafting cutting-edge data science solutions that genuinely make parents' lives easier. If you're ready for an exciting adventure with our passionate team, don’t hesitate to apply today!

Frequently Asked Questions (FAQs) for Hybrid Data Scientist & ML/AI Engineer Role at Mumsnet
What are the responsibilities of the Hybrid Data Scientist & ML/AI Engineer at Mumsnet?

As a Hybrid Data Scientist & ML/AI Engineer at Mumsnet, your primary responsibilities will include designing and deploying machine learning models, leveraging AI technologies, and developing AI-driven data products. You'll collaborate with multiple teams to transform requirements into functional data solutions, ensuring optimal performance and scalability. Your role will also involve maintaining and enhancing MumsGPT, our in-house AI platform, while engaging with stakeholders to present insights and recommendations.

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What qualifications are required for the Hybrid Data Scientist & ML/AI Engineer position at Mumsnet?

Candidates for the Hybrid Data Scientist & ML/AI Engineer role at Mumsnet should have a strong proficiency in Python and its relevant libraries, alongside experience in deploying machine learning models in production environments. Additionally, familiarity with large language models, APIs, and data visualization tools is critical for success. A solid understanding of MLOps best practices is also beneficial for this position.

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How does Mumsnet support the professional development of its Hybrid Data Scientist & ML/AI Engineer?

At Mumsnet, we prioritize your growth with ongoing learning opportunities. You'll have regular 1-1 meetings with your line manager for progress reviews, and participate in bi-weekly 'Drop Everything And Learn' sessions. Plus, our buddy and mentor programs enhance your development, ensuring you have the support you need as you navigate your career path in data science and AI.

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What does the work-life balance look like for the Hybrid Data Scientist & ML/AI Engineer at Mumsnet?

The Hybrid Data Scientist & ML/AI Engineer at Mumsnet enjoys a fantastic work-life balance. We truly value flexibility, allowing you to work primarily from home and only visit the office two days a week. Our culture emphasizes output over hours spent working, enabling you to manage personal commitments like school runs while still contributing effectively to your projects.

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What innovative projects can a Hybrid Data Scientist & ML/AI Engineer expect to work on at Mumsnet?

In the role of Hybrid Data Scientist & ML/AI Engineer at Mumsnet, you’ll engage in exciting projects using web traffic analytics, NLP applications, and AI-driven content moderation. You’ll play an integral role in maintaining and enhancing our unique AI platform, MumsGPT, which streamlines data analysis for our users. Everyday tasks will vary, ensuring that no two days are alike as you help create groundbreaking data products.

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Common Interview Questions for Hybrid Data Scientist & ML/AI Engineer
Can you explain your experience with machine learning model deployment?

In preparing for this question, emphasize your hands-on experience with deploying machine learning models in production environments. Discuss the frameworks you've worked with, your involvement in performance testing, and any monitoring strategies you've employed post-deployment. Providing specific examples of projects can showcase your expertise effectively.

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How do you approach problem-solving in data science?

When responding to this question, describe your systematic approach to data-driven problem-solving. Emphasize the importance of understanding the business context, gathering relevant data, and analyzing it thoroughly before developing models. Illustrate with a specific example of a challenge you've faced and how your approach led to a successful outcome.

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What AI technologies do you have experience with?

Here, showcase your exposure to various AI technologies like large language models, computer vision frameworks, or neural networks. Specify any projects where you've utilized these technologies and discuss the impact they had on the outcomes. This demonstrates your practical understanding and enthusiasm for implementing advanced AI techniques.

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How do you collaborate with non-technical stakeholders?

To answer this question, share your strategy for bridging the gap between technical and non-technical audiences. Talk about your methods for breaking down complex data concepts into understandable terms and the importance of using visualizations to communicate insights effectively. Providing an anecdote about a successful collaboration can add depth to your response.

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What best practices do you follow for MLOps?

In your response, outline your understanding of MLOps and why it’s essential for successful machine learning deployments. Discuss the significance of version control, continuous integration, and automated testing. Mention your experience with tools like Docker or cloud platforms, and how these practices improve model performance and maintenance.

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Can you describe a project where you used NLP?

When answering this question, focus on a specific NLP project you've worked on, detailing the objectives, techniques used, and results. Explain your choice of algorithms, how you preprocess the data, and the insights gleaned from the outcome. Highlighting a successful project demonstrates your capability and relevance to the role.

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How do you prioritize tasks when working on multiple data projects?

In your response, emphasize your organizational skills, using prioritization frameworks or tools you utilize for managing multiple projects. Discuss how you assess urgency versus importance when allocating your time and energy among tasks to ensure all projects progress effectively without compromising quality.

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What role do you see data science playing in product development?

Answer this by discussing how data science is integral to enhancing product functionality and user experience. Offer insights on how data-driven decision-making informs feature design and user engagement. Use examples of data products from Mumsnet or your previous experiences to illustrate your point.

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What methods do you use to ensure data quality?

Address this question by explaining your approach to data validation and cleansing. Discuss techniques such as data profiling, consistency checks, and anomaly detection processes you integrate into your workflows. Highlighting your proactive stance on data quality ensures your analyses and models are reliable.

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What motivates you to work in the field of data science and AI?

When discussing your motivation, share your passion for utilizing data to solve real-world problems and the potential of AI to drive innovation. Reflect on how the ability to empower organizations and communities through data impacts your enthusiasm for the field and inspires continuous learning.

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Full-time, hybrid
DATE POSTED
March 13, 2025

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