Carbon Data Analyst

TieTalent
Nuneaton
1 month ago
Create job alert

Overview

Join to apply for the Carbon Data Analyst role at TieTalent.


Location: Field Based


Contract Type: Permanent


Hours: Full time


Salary: £30,000 per annum


Benefits: 30 days holiday, pension, life assurance, employee assistance programme, wellbeing support, and flexible benefits scheme


About the Job

You’ll have an important part to play enhancing the understanding and facilitating informed decision-making regarding carbon emissions across Unipart and the wider supply chain. You’ll conduct in-depth analysis to refine the accuracy and quality of the carbon data as well as supporting the integration and utilisation in the way the carbon accounting is performed.


Here at Unipart, we don’t just have a way of working, we have The Unipart Way. It allows everyone in our team to pursue their own personal and professional goals to a world class level, through Unipart's ‘From Gate to Great’ training and development program.


As a Carbon Data Analyst, you will produce reports that demonstrate carbon performance aligned to targets and wider net-zero strategy. You’ll promote the principle and consciousness that effective management of environmental sustainability is an integral part of efficient business and operational management.


Responsibilities


  • Capture and collate data across Scope 1, 2 and relevant scope 3 categories
  • Ensure all relevant CO2 emissions are accounted for, identifying wider opportunities to improve on earlier less granular reporting by attaining better data quality or changing the calculation methodology
  • Prepare standard reports for the presentation of the collected data, monthly, quarterly and annually, including ad-hoc requests
  • Provide appropriate documentation to meet necessary reporting requirements for all stakeholders
  • Collate, analyse and report on energy, waste, water, CO2 emissions and single use plastic as part of internal and external reporting requirements
  • Report to customer and internal reviews and meetings as required on all environmental and sustainability topics


About You

We’d love you to have the following skills and experience, but please apply if you think you’d be able to perform well in this role!



  • Experience in a similar role with industry standards and regulations
  • IT literate in Microsoft Office and Google Suite including advanced Excel (PivotTables & VLookup) and Google Sheet skills; Javascript skills advantageous
  • Ability to leverage different technologies, such as Java, APIs, and data visualisation platforms
  • Ability to create online content on Google Sites platforms
  • Confident, self-motivated with strong presentation skills
  • Attention to detail and accurate data entry
  • Adaptable and able to react quickly and effectively to changes and requests
  • Works well as part of a team with good leadership skills
  • An understanding of WMS
  • Quality and customer focused
  • Strong communicator using a range of methods
  • Committed to self-development and team development
  • Proactive and solution-oriented mindset, with a focus on continuous improvement
  • Business-savvy approach to data and its uses
  • Ability to translate complex data concepts
  • Ability to work independently, apply your own initiative and meet deadlines
  • Logical approach to dealing with ambiguous situations


Equal Opportunity

Our recruitment and selection process has been developed to ensure that it is consistent, fair and provides equality of opportunity - all selection decisions are based solely on technical and behavioural competencies. We do not discriminate on the grounds of race, colour, or nationality, ethnic or national origins, sex, gender reassignment, sexual orientation, marital or civil partnership status, pregnancy or maternity, disability, religion or belief, age or any other current or future protected characteristic as defined in the current Equality Act of England and Wales. As an organisation we also promote an environment which encourages diversity of characteristics and thought, where you feel included, safe and confident to be the best version of yourself and do your best work every day.


You may also have experience in the following: Carbon Data Analyst, Carbon Accounting, Environmental Data Analyst, Sustainability Analyst, Net-Zero Strategy, Scope 1, 2, and 3 Emissions, ESG (Environmental, Social, and Governance) Reporting, Environmental Sustainability, CO2 Emissions Tracking, Carbon Footprint Analysis, Environmental Regulations, Regulatory Compliance, Data Quality & Accuracy.


#J-18808-Ljbffr

Related Jobs

View all jobs

Energy & Carbon Data Analyst | Regulatory Reporting & Renewables

CBAM Data Analyst

Data Analyst - Power BI Specialist

Lead Data Analyst and Insights

Lead Data Analyst and Insights

Senior Power BI Data Analyst

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.