Data Scientist - Monitoring & Alerting Infrastructure

TieTalent
Manchester
6 months ago
Create job alert

Overview

Data Scientist - Monitoring & Alerting Infrastructure: Our client is looking for a Data Scientist to help build and mature monitoring and alerting infrastructure, centralising efforts and enabling scalable, standardised approaches across multiple projects. This is a pivotal role for someone who thrives in autonomous environments and enjoys owning solutions from concept to deployment.

Responsibilities

  • Design and implement monitoring and alerting systems to ensure the reliability and accuracy of key datasets and processes.
  • Collaborate with teams to define relevant metrics, thresholds, and KPIs.
  • Build, maintain, and productionise machine learning and statistical models using Python and PySpark.
  • Deploy monitoring tools and models using AWS infrastructure.
  • Create scalable frameworks for future monitoring requirements across products and teams.
  • Investigate and troubleshoot anomalies in the data pipeline.
  • Promote data quality and monitoring best practices across the business.
  • Mentor junior team members and contribute to a culture of curiosity, rigour, and innovation.
  • Adhere to Company Policies and Procedures with respect to Security, Quality and Health & Safety.

About You / Qualifications

  • Proficiency in Python and SQL for analysis, model development, and data interrogation.
  • Experience in handling large datasets with PySpark and managing distributed data processing.
  • Comfortable deploying statistical or ML models into production environments.
  • Strong understanding of cloud infrastructure, preferably AWS.
  • A methodical, problem-solving mindset with high attention to detail.
  • Able to scope, define, and deliver complex solutions independently.
  • Comfortable working closely with non-technical stakeholders to define business-critical metrics.
  • Self-motivated, accountable, and keen to continuously learn and grow.
  • Previous experience building monitoring or data quality frameworks is highly desirable.

Benefits

  • Generous Time Off: 25 days of paid holiday, plus bank holidays. After two years, you can buy or sell up to 5 days of annual leave.
  • Life assurance and a workplace pension with employer contributions.
  • Bonus scheme that recognizes your hard work and contributions.
  • Cycle to Work Scheme.
  • Choice of equipment to suit you.
  • Learning & Growth: coaching, training budget, and support for ongoing development.
  • Giving Back: opportunities to support local charities.

Working Pattern

  • Hybrid working model with a Manchester office: office space, free parking, secure bike shed, good public transport links.
  • Split time between office and home with full equipment provided for home working (desk, screen, chair).
  • £100 annually to personalise your home workspace.
  • Flexible start and finish times.

Additional Details

  • Seniority level: Mid-Senior level
  • Employment type: Full-time
  • Job function: Engineering and Information Technology
  • Industries: Technology, Information and Internet


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist - SC Cleared

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.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.