Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Junior Data Scientist

Fynity
London
1 year ago
Applications closed

Related Jobs

View all jobs

Junior Data Scientist

Junior Data Scientist

Junior Data Scientist

Junior Data Scientist

Junior Data Scientist

Lead Data Scientist

Junior Data Scientist Location: London Job Type: Full-time Salary: CompetitiveA leading technology organisation is seeking a motivated Junior Data Scientist to join their dynamic team. This entry-level position is an excellent opportunity for individuals looking to develop their skills and gain hands-on experience in data science while contributing to projects that drive business impact across various industries, including healthcare, retail, logistics, finance, and digital transformation.This technology company specialises in providing data-driven solutions and software development services across a range of sectors. Their offerings include the creation of websites, mobile applications, and SaaS products designed to fulfil specific business objectives, such as enhancing customer engagement, optimising operational efficiency, and driving sales growth.Key Responsibilities:Support Data Science Projects: Assist in the end-to-end lifecycle of data science projects, including data collection, preprocessing, and analysis, while learning to apply machine learning techniques.Model Development: Collaborate with senior team members to design and implement machine learning models that address business challenges, gaining exposure to advanced algorithms and methodologies.Data Analysis: Conduct exploratory data analysis (EDA) to identify trends, patterns, and insights from data, contributing to the strategic initiatives of the company.Collaboration: Work closely with cross-functional teams, including data engineers and product managers, to ensure alignment on project goals and deliverables.Documentation and Reporting: Help document processes and findings, creating clear reports and visualisations that communicate results to technical and non-technical stakeholders.Continuous Learning: Stay informed about industry trends and new technologies in data science and machine learning, actively seeking opportunities to expand your skill set.  Key Requirements:Education: A degree in a relevant field such as Computer Science, Statistics, Mathematics, or Data Science is preferred.Experience: 0-2 years of relevant hands-on experience in data science or related fields, including internships or co-op placements that involved practical application of data analysis and machine learning techniques.Technical Skills: Proficiency in programming languages such as Python or R. Familiarity with machine learning libraries (e.g., scikit-learn) and data manipulation tools (e.g., Pandas) is a plus.Data Management: Understanding of SQL and experience with data analysis and visualisation tools (e.g., Tableau, Matplotlib).Analytical Skills: Strong problem-solving abilities and a passion for data analysis and insights.Soft Skills: Effective communication skills, a willingness to learn, and the ability to work collaboratively within a team.If you're ready to kickstart your career in data science and you meet the qualifications, please send your CV to us ASAP!If you are interested please apply ASAP. The People Network is an employment agency and will respond to all applicants within three - five working days. If you do not hear within these timescales please feel free to get in touch

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.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.