Data Analytics Manger

Adler & Allan Ltd
London
1 month ago
Applications closed

Related Jobs

View all jobs

Audit Data Analytics Manager

Data Analytics and Reporting Manager

Data Science and Analytics Manager

Senior Business Intelligence Manager

Data Analytics Lead – London /Remote

People Data Governance Manager

Company Description

Detectronic is a UK-based technology company specialising in wastewater and water monitoring solutions . Founded in 2007 , the company designs, manufactures, and installs a range of ultrasonic wastewater flow, level, and water-quality monitoring equipment , serving the water industry and various commercial sectors.

Mission & Purpose

Detectronic focuses on improving environmental outcomes by enabling organisations to: Prevent sewer flooding
Reduce pollution
Improve river and bathing-water quality
Maintain compliance with environmental regulations
Job Description

Data Analytics Manager
Nelson
12 Month Fixed Term Contract

We are seeking a highly skilled and experienced Data Analytics Manager to join our team in Nelson. As a key member of our organisation, you will lead our data analytics efforts, driving data-driven decision-making across the company.

More about the role: Develop and implement data analytics strategies aligned with business objectives
Lead and mentor a team of data analysts, fostering a culture of continuous improvement
Design and oversee complex data analytics projects, ensuring timely and accurate delivery
Collaborate with cross-functional teams to identify data needs and provide actionable insights
Implement best practices for data governance, quality, and security
Translate complex analytical findings into clear, actionable recommendations for stakeholders
Stay current with emerging trends and technologies in data analytics and machine learning
Help develop and maintain dashboards and reports to monitor key performance indicators
Optimize data collection procedures and refine data analytics processes for efficiency
Present findings and insights to executive leadership to inform strategic decision-making
Qualifications

About you: Bachelor's degree in Statistics, Computer Science, Mathematics, or related field; Master's degree preferred
3+ years of experience in data analytics, with a proven track record of leading successful projects
In-depth knowledge of statistical analysis, machine learning, and predictive modeling techniques
Experience leading and mentoring teams of data analysts
Excellent project management skills with the ability to manage multiple projects simultaneously
Strong business acumen and ability to translate data insights into strategic recommendations
Proven experience in implementing data governance and quality control measures
Outstanding communication skills, both written and verbal, with the ability to present complex information to diverse audiences
Experience with big data technologies and cloud-based analytics platforms is a plus
Certifications in relevant data analytics or project management fields are desirable
Additional Information

What's in it for you?: Enhanced maternity, paternity and adoption pay and leave
Company pension
Life assurance scheme (x4 salary)
Medicash plan (includes cash payments towards dental, medical, therapeutic treatments) with the option to add up to 4 dependants.
Refer a friend scheme
Employee assistance programme (access to GP appointments and mental health support)
Competitive annual leave plus bank holidays
Training and career progression opportunities
Adler and Allan are committed to fostering diversity and inclusion in our workplace. We proudly embrace equal opportunities for all applicants, regardless of race, colour, religion, sex, sexual orientation, gender identity or national origin. If you require any support with your application, whatever the circumstance, please let us know.
TPBN1_UKTJ

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.