Data Analyst

iVerify Enterprise
Belfast
1 week ago
Create job alert
About iVerify

We are Experts in Mobile Threat Hunting. The first mobile threat hunting company to protect mobile devices like any other vulnerable corporate endpoint.


The mobile security market has a problem. Simply put, current solutions fail to meet the sophistication of modern threats or the growing privacy desires of mobile device users. We believe that it is time for something new. Not only because we care deeply about the safety of frontline users like journalists and activists - many of whom are doing important and often dangerous work - but because enterprises and consumers deserve real protection from advanced mobile threats without sacrificing privacy.


We are building the first mobile threat hunting company to harmonize security and privacy in the face of a new class of mobile threats. Supported by some of the most well-respected VC firms, we aim to become the go-to mobile security solution for individuals who want to know they can trust their devices with their most sensitive information – without sacrificing privacy.


About the Role

We are seeking a Data Analyst to help transform iVerify’s growing dataset into actionable insights that guide engineering, research, and product decisions.


This role sits at the intersection of data, security research, and product development, helping uncover meaningful patterns in mobile telemetry, detect anomalies, and guide decision-making across the organization.


You’ll analyze structured and semi-structured datasets from millions of mobile events, build dashboards, track key performance metrics, and help our researchers identify new threat behaviors and trends across iOS and Android devices.


Your work will directly empower our security research team to focus on investigations and detection, while ensuring our engineering team builds from data-driven insights. You’ll also help translate complex telemetry into product-level intelligence, uncover trends that inform detection strategy, and surface insights that shape how iVerify evolves its threat models and platform capabilities.


As one of the foundational members of iVerify’s data function, you’ll shape our analytics stack, define best practices, and build the pipelines and dashboards that scale with the company’s growth.


Key Responsibilities

  • Data Exploration & Analysis: Analyze large, complex mobile telemetry datasets to identify trends, anomalies, and threat patterns.


  • Dashboarding & Visualization: Build and maintain dashboards tracking platform performance, detection efficacy, and telemetry coverage.


  • Research Support: Collaborate with the security research and detection teams to design data-driven experiments and surface emerging patterns of interest.


  • Product Insights: Translate complex data into clear, actionable insights that inform product decisions, customer reporting, and platform improvements.


  • Data Quality & Validation: Monitor the completeness and consistency of incoming data streams. Highlight quality issues and collaborate with the data engineering team to resolve them.


  • Reporting & Automation: Develop repeatable reporting processes and automated queries to track key performance indicators across product and detection metrics.


  • Collaboration: Work cross-functionally with engineering, machine learning, research, and customer success teams to make data accessible, explain findings, and support decisions with evidence.


  • Security Analytics Enablement: Support the creation of data-driven detections and models by providing clean, validated datasets and exploratory findings.



Day-to-Day Activities

  • Use Python and the modern data science stack (Pandas, Jupyter, NumPy, Matplotlib, or equivalent) to perform deeper statistical or exploratory analysis.


  • Build visualizations and dashboards in Tableau, Power BI, Looker Studio, or similar BI tools.


  • Collaborate with researchers to validate hypotheses about threat activity or telemetry anomalies.


  • Conduct ad hoc analyses to support product and detection improvements.


  • Document key findings and communicate insights to both technical and non-technical audiences.


  • Partner with data engineers to improve dataset availability, structure, and performance for analytical workloads.


  • Work with the ML team to analyze model outputs, validate detection metrics, and support ongoing model evaluation efforts.



Requirements

  • 4+ years of experience as a Data Analyst, Product Analyst, or similar analytical role.


  • Advanced SQL skills and hands-on experience working with large datasets.


  • Strong Python proficiency for data analysis (Pandas, NumPy, Jupyter).


  • Experience building reports and visualizations with Tableau, Power BI, or equivalent BI tools.


  • Proven ability to identify trends, anomalies, and correlations within complex datasets.


  • Experience working cross-functionally with engineering, research, and product teams.


  • Excellent communication skills, able to translate technical findings into actionable business or product insights.


  • Nice to Have: Experience working with security or threat intelligence data, telemetry, or log-based systems.


  • Nice to Have: Familiarity with modern data infrastructure, such as cloud data warehouses and version-controlled analytics tools.



Compensation

Our salary ranges are determined by role, level, location, and employment type. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position. Within the range, individual pay is determined by a variety of factors, including, but not limited to, work location, job-related skills, experience, and relevant education or training.


Diversity, Equity, and Inclusion

At iVerify, we are committed to building a diverse, equitable, and inclusive workplace and community. We believe that diversity in all its forms drives innovation and fosters creativity. We strive to create an environment where everyone feels valued, respected, and empowered to bring their authentic selves to work.



#J-18808-Ljbffr

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

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.

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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.