Data Scientist (Full Stack)

Humara
Brighton
4 days ago
Create job alert

This mid-level data science role is based within a cross-functional delivery team, working on our groundbreaking genAI product. The successful applicant will be crucial in collaboratively researching/modelling/building features to personalise and power intelligent user interactions.


Your responsibilities will include hands-on product development, adherence to industry best practices, including model development and deployment, and the use of techniques such as reinforcement learning to improve product performance.


You will have solid foundations in machine learning theory and practical experience, particularly in NLP, is essential for success. We are also looking to push the performance boundaries of our new product as far as possible, so experience and knowledge of techniques like reinforcement learning would be a bonus.


You will need to be confident in writing production-ready Python code, building end-to-end functionality, and deploying it to a live environment. Experience working within a professional software development setting is essential.


Responsibilities

  • Develop, train, and deploy machine learning and AI models, with a focus on NLP and language understanding tasks.
  • Write production-grade Python code to build functionality and deploy AI systems to production.
  • Work extensively with PyTorch and other machine learning frameworks to build and iterate on models.
  • Optimise and productionise models inside the AWS ecosystem, using accelerated hardware resources where needed.
  • Build intelligent guardrails to protect our users, product and customers.
  • Collaborate closely with cross-functional teams, including other data scientists, product and machine learning engineers, to integrate AI solutions into our tech stack.
  • Explore and implement cutting-edge techniques like reinforcement learning and LLM fine-tuning.
  • Explore and implement methods to measure product performance and gain insights into performance metrics.
  • Documentation and active knowledge sharing.
  • Cross-functional team collaboration.
  • Adherence to best practices, including code quality and security.
  • Continuous learning and development.
  • Responding to alerts from monitoring systems on models or technology in the data science domain (during work hours).

Requirements

  • Experience in data science and machine learning, with a proven track record of deploying models in production settings.
  • Proficiency in writing production-grade Python
  • Familiarity with machine learning and deep learning frameworks (e.g. Scikit-learn, PyTorch, TensorFlow).
  • Experience with web development frameworks (Django, FastAPI).
  • Experience with containerisation technologies (e.g., Docker, ECR) and an understanding of GPU acceleration for deep learning.
  • Experience working in a software engineering environment
  • Experience with microservice design patterns.
  • Experience in a range of machine learning techniques, such as:

    • NLP techniques like text embeddings, large language models and entity & intent recognition.
    • Reinforcement learning algorithms and applications.
    • Recommendation techniques and algorithms.
    • Supervised and unsupervised machine learning techniques.
    • Prediction and uplift modelling techniques.

  • Experience with agentic, RAG, required; council orchestration understanding, beneficial.
  • Previous exposure to sales funnel optimisation, sales and marketing insights, sales psychology and its application in data-driven contexts is beneficial.
  • Excellent communication skills, with the ability to clearly articulate technical concepts to non-technical stakeholders

Studies have shown that women and people who are disabled, LGBTQ+, neurodiverse or from ethnic minority backgrounds are less likely to apply for jobs unless they meet every single qualification and criteria. We're committed to building a diverse, inclusive, and authentic workplace where everyone can be their best, so if you're excited about this role but your past experience doesn't align perfectly with every requirement on the Job Description, please apply anyway - you may just be the right candidate for this or other roles in our wider team.


Benefits

  • Medicash healthcare scheme (reclaim costs for dental, physiotherapy, osteopathy and optical care)
  • Life Insurance scheme
  • 25 days holiday + bank holidays + your birthday off (rising to 28 after 3 consecutive years with the business & 30 after 5 years)
  • Employee Assistance Programme (confidential counselling)
  • Gogeta nursery salary sacrifice scheme (save up to 40% per year)
  • Enhanced parental leave and pay including 26 weeks’ full maternity pay and 8 weeks’ paternity leave


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist - Workforce Modelling

Data Scientist - Imaging - Remote - Outside IR35

Data Scientist (Predictive Modelling) – NHS

Data Scientist

Data Scientist

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

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.