Cost Data Analyst - Senior Cost Manager

Turner & Townsend
Newcastle upon Tyne
2 months ago
Applications closed

Related Jobs

View all jobs

Program Cost and Data Analyst

Data Analyst Senior (#CJ)

Cost Control & Data Analyst

Cost Control & Data Analyst — Power BI Dashboards & Cashflow

Data Analyst

Business Data Analyst

Cost Data Analyst - Senior Cost Manager

Join to apply for the cost data analyst – senior cost manager role at Turner & Townsend.


Turner & Townsend is a global professional services company with over 22,000 people in more than 60 countries. Working across real‑estate, infrastructure, energy and natural resources, we transform outcomes that improve people’s lives. We offer expertise in programme, project, cost, asset and commercial management, controls and performance, procurement and supply chain, net zero and digital solutions.


Role Purpose: Supporting the Operations Lead and Technical Lead, this role focuses on ensuring the effective use of data tools and adherence to policies, driving operational excellence through data‑driven insights.


Key Responsibilities

  • Foster a collaborative and productive work environment.
  • Understand previous processes, lessons learned and embed them into new processes and tools.
  • Undertake the collection, processing, and analysis of data, engaging with Trusts and client finance teams to integrate their inputs.
  • Ensure data accuracy, consistency and integrity through robust processes, tools and stakeholder engagement.
  • Develop and maintain current templates and tools, including roll‑out and training as required.
  • Utilise data tools to generate actionable insights and trends and feed them back into the programme.
  • Create and maintain dashboards and reports to support decision‑making.
  • Implement and manage data tools and software.
  • Ensure compliance with data policies and regulations.
  • Continuously evaluate and improve data tools and processes, engaging with various stakeholders and client departments to capture lessons learned and identify effective improvements.

Qualifications

  • Data analysis and visualisation.
  • Excellent Excel skills.
  • Policy implementation and compliance experience.
  • Effective communication and collaboration.
  • Attention to detail and accuracy.

SOX control responsibilities may be part of this role, which are to be adhered to where applicable.


Turner & Townsend is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees and actively encourage applications from all sectors of the community.


It is strictly against Turner & Townsend policy for candidates to pay any fee in relation to our recruitment process. No recruitment agency working with Turner & Townsend will ask candidates to pay a fee at any time. Any unsolicited resumes/CVs submitted through our website or to Turner & Townsend personal e‑mail accounts are considered property of Turner & Townsend and are not subject to payment of agency fees.



#J-18808-Ljbffr

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 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.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.