Senior Data Analyst

Humand Talent
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst - Customer Services

Senior Data Engineer

Senior Data Engineer (AWS, Airflow, Python)

Senior Data AnalystAre you ready to turn data intoimpactful insights that drive success in an innovative andfast-moving industry?Do you thrive on leveraging technicalexpertise to influence business outcomes?If youre looking or yournext big career opportunity, this role is tailor-made for you!WhyThis Opportunity Stands OutJoin a forward-thinking organisation asa Senior Data Analyst, where your contributions will empowerstrategic decisions and fuel business growth. This is your chanceto play a pivotal role in a sector where data drives innovation.Immerse yourself in a dynamic environment with a vibrant workplaceculture, all while honing your skills and making a tangibleimpact.What You’ll DoShape Data-Driven Strategies: Develop andimplement robust data models, ETL pipelines, and visualisationtools to guide business strategies.Deliver Business Insights:Partner with stakeholders to understand challenges, translatingdata into actionable insights that optimise performance.PromoteData Empowerment: Drive self-service analytics by buildingintuitive tools and fostering data literacy across theorganisation.Inspire and Lead: Share your expertise to mentorjunior analysts, fostering a culture of collaboration andcontinuous improvement.Perks and BenefitsThis role offers more thanjust a job – it’s an opportunity to thrive in an enriching andsupportive environment.Work-Life Balance: Flexible workingarrangements that allow you to excel professionally whilemaintaining personal priorities.Unbeatable Perks: Enjoy in-houseculinary delights, gym memberships, healthcare packages, and stockoptions.Innovative Team Culture: Be part of a transparent,creative, and supportive team committed to progress andsuccess.What You Bring to the TableYou’re a results-driven dataenthusiast with a passion for problem-solving and collaboration.Here’s what we’re looking for:Technical Expertise: Proficiency inPython, SQL, and data visualisation tools like Tableau or Power BI,paired with experience in relational databases.Strategic Mindset: Aproven ability to connect analytical insights to broader businessobjectives.Leadership Skills: A collaborative spirit with thedesire to lead, coach, and inspire others.Added Advantage:Certifications like CDS or CAP are a bonus but not arequirement.Ready to Take the Leap?If you’re passionate abouttransforming data into action, building strong relationships, anddriving success in an exciting industry, we’d love to hear fromyou! This is your chance to make a meaningful impact and grow yourcareer in a supportive and forward-thinkingenvironment

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.