Senior learning Specialist - Data Science

Cheltenham

1 year ago

Applications closed

Related Jobs

View all jobs

Senior Data Scientist and Machine Learning Researcher

Senior Data Science Consultant

Senior Data Engineering Consultant

Senior Data Analyst

Data Engineer - Newcastle

Lead Data Engineer

Role: Technical Trainer - Data Science

Location: Commutable London/Gloucester daily

Working environment: In-person Training, travel is required

Contract: Full time, 37.5 hrs per week

Package: competitive + benefits

Role Description:

Are you an experienced Technical Trainer with a strong background in practical data science, and ideally experience of teaching programming languages including Python and C.

You will be passionate about education, possess excellent communication skills, and have a proven track record of success. As a Technical Trainer, you will play a crucial role in empowering our learners with the skills and knowledge needed to excel in the rapidly evolving field of data science

Key Responsibilities:

Instruction and Delivery:

Conduct engaging and hands-on training sessions, workshops, and seminars for both non-data scientists and experienced data scientists. Deliver training content effectively, ensuring that participants gain practical skills and knowledge applicable to their roles.

Curriculum Development:

Design and develop comprehensive training programs focused on practical data science, tailored to meet the needs of our learners.

Technical Expertise:

Demonstrate a deep understanding of data science principles, Python programming, and proficiency in C. Share real-world examples and experiences from a software engineering environment to enhance the practical relevance of training content.

Assessment and Feedback:

Provide constructive feedback to participants, identifying areas for improvement and additional support.

Collaboration:

Work closely with cross-functional teams, including sales, and projects, to align training programs with customer goals.

Continuous Learning:

Stay abreast of industry trends, emerging technologies, and best practices to ensure training content remains current and relevant.

Qualifications:

Educated/Certified in Computer Science, Data Science, or a related field or equivalent industry experience. Proven experience as a Technical Trainer with a focus on data science, Python, and C. Strong programming skills in Python and C, with a solid understanding of software engineering principles.

Use of some of the following tools:

Visual Studio Jupyter Notebooks Git Gitlabs Docker Kebernates Apache Spark MatLab TensorFlow

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Feb 10, 2026

Careers

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.

Feb 3, 2026

Jobs

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.

Jan 23, 2026

Education

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.