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

Apply Now

Data Analyst/Engineer - Arlanis UK

Reply
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst / Engineer

Big Data Analyst

Construction Data Analyst

Construction Data Analyst

Construction Data Analyst

Senior Data Analyst - Electronics Engineering Manufacturing

Responsibilities

: Gather qualitative data from the Client's sales team 'chatter' and integrate it into your data analysis to inform your sales targeting and forecast reports Continuously analyse the data within the Client's Salesforce database for quality,pliance and make rmendations to fill gaps and improve its usefulness and effectiveness Use Salesforce's Einstein Tableau CRM suite of tools to configure reports that give you the information you need to inform the performance of past sales, the salient learning points and sales levers for conversion and identify segments for future targeting and nurture campaigns Using qualitative and quantitative data analysis, build reports and dashboards to support the client sales team targets, identifying opportunities within the underlying CRM database that define marketing focus Liaise closely with the internal CRM and MA teams to support agreed customer and prospect nurture campaigns, using the findings of your analysis Share your findings with your colleagues in the Strategy and Insight team so that they have the opportunity to augment and support your findings with salient marketing context or product detail that will bring extra resonance to the client sales teamAbout the candidate:You have achieved a min Bachelor's/Master's degree inputing, IT or in a business-related field with exposure to technology Solid background as a Data Analyst/Engineer with Salesforce and Salesforce Einstein Tableau CRM suite experience to generate reports and forecasting Proficient in Python, CRMA and data manipulation languages with experience in data visualisation Great understanding of data protection legislation and the responsibilities of any role with access to process data which may contain personal information as well as knowledge of the technical and organizational measures that need to be in place to protect data held in databases Good writtenmunication skills used to translate the findings from your analysis into easily understood summaries and rmended actions Reply is an Equal Opportunities Employer andmitted to embracing diversity in the workplace. We provide equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type regardless of age, sexual orientation, gender, identity, pregnancy, religion, nationality, ethnic origin, disability, medical history, skin colour, marital status or parental status or any other characteristic protected by the Law.

Reply ismitted to making sure that our selection methods are fair to everyone. To help you during the recruitment process, please let us know of any Reasonable Adjustments you may need.
Job ID 10176

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.

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

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.