Data Analytics Intern

Hirist
Liverpool
6 months ago
Applications closed

Related Jobs

View all jobs

Data Analytics & C#.net engineer-financial services

GCP Data Engineer

GCP Data Engineer

Developer with Data Engineering focus

Data Quality and Governance Manager

Data Analyst – Demand Planning & Supply Chain

Data Analytics Intern (Remote – 3 Month Internship)


Company:


HIRIST – IT Recruitment Partner (Hiring for a Technology Client)


Location:

Remote | United Kingdom


Job Type:

Internship


About the Role:

HIRIST is hiringData Analytics Internson behalf of a reputed IT client. This is a part-time, remote internship opportunity designed for individuals looking to apply data analytics skills in a business setting. Interns will work with the analytics team to support ongoing data projects that drive decision-making.


Key Responsibilities:

  • Clean and organize datasets for analysis.
  • Assist in generating dashboards and reports.
  • Perform basic data exploration to support business insights.
  • Use tools like Excel, SQL, and analytics platforms.
  • Contribute to ad-hoc reporting tasks as assigned.


Required Qualifications:

  • Familiarity with Excel or Google Sheets.
  • Basic knowledge of SQL.
  • Attention to detail and strong analytical thinking.
  • Ability to work independently in a remote setup.


Preferred Qualifications:

  • Exposure to visualization tools like Power BI, Tableau, or Looker.
  • Experience with Python for data analysis (e.g., Pandas, Matplotlib).
  • Previous academic or personal projects involving data interpretation.


Internship Details:

  • Duration: 3 months
  • Hours: 15–20 hours/week
  • This is a paid internship. A stipend will be provided.
  • Certificate of completion provided upon successful completion.


Hiring Process:

  1. Resume Review
  2. Basic Data Task
  3. Virtual Interview with Project Team
  4. Onboarding via HIRIST


Additional Information:

  • Remote position — stable internet connection required.
  • HIRIST is a recruitment partner facilitating hiring for its verified IT client.
  • The client organization’s name will be shared with shortlisted candidates during the interview process.
  • There areno feesor charges involved at any point in the selection process.


Equal Opportunity Statement:

HIRIST is an equal opportunity recruitment partner. We welcome applications from candidates of all backgrounds, without regard to race, gender, disability, or other protected characteristics.

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.