Lead Machine learning Engineer

Harnham
Bristol
1 year ago
Applications closed

Related Jobs

View all jobs

Head of Data Science

Head of Data Science

Principal Data Scientist London, United Kingdom

Manager - Data and Data Science Strategy - Emerging Data and Capabilities

Manager – Data and Data Science Strategy – Emerging Data and Capabilities

Manager - Data and Data Science Strategy - Emerging Data and Capabilities

LEAD MLOPs ENGINEER

Up to £90,000 + 10% bonus, car allowance and benefits

REMOTE (London once a month)


This is a chance to join a leading Telecomms company as a part of their Data Science team help build and deploy impactful models and work with cutting-edge technologies. They are looking for a Lead MLE to work end to end, building and deploying models.


ROLE:

Your day-to-day responsibilities will include:


  • Building, deploying and productionising segmentation, churn, and recommender system-based projects, alongside deep learning and neural networks to support core internal projects
  • Part of a team of 7 reporting to the Head of Data Science
  • Chance to upskill and mentor juniors whilst remaining fully hands-on
  • Focusing on end-to-end data pipelines, for training, evaluating and deploying ML models
  • Working closely with Data Scientists on client partners, advising on best practice ML and MLOps infrastructure
  • Driving best practices in a fast-paced environment, within a well-established company


REQUIREMENTS:

  • MSc or PhD level education in STEM subjects.
  • Strong experience in building and deploying ML models
  • Preference for experience in customer modelling but not required
  • Candidates should be looking to work in a fast paced startup feel environment
  • Tech across: Python, SQL, AWS, Databricks, PySpark, AB Testing, MLFlow, APIs


If this role looks of interest, please reach out to Joseph Gregory.

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 Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.