Senior Data Analyst - Content Indexing

Bloomberg
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist - Game Analytics

Principal Data Scientist

Principal Data Scientist

Senior Data Science Analyst - Shipping

Senior AI Data Scientist

Data Scientist

Senior Data Analyst - Content IndexingBloomberg runs on data. Our products are fueled by powerful information. Webine data and context to paint the whole picture for our clients, around the clock - from around the world. In Data, we are responsible for delivering this data, news and analytics through innovative technology - quickly and accurately. We apply problem-solving skills to identify innovative workflow efficiencies, and we implement technology solutions to enhance our systems, products and processes.

As a News Content Indexing Data Interpreter, you are the bridge that connects our technical aspirations with real world applications. You play a crucial role in ensuring the quality of our annotation data and developing our news classification systems to better serve our clients. Your role expands beyond ensuring automated classification systems for relevant financial and economic news articles. Collaborating with a range of teams, you will shape the product strategy specific to the global markets. Your insights will influence how we expand, ensuring that our product resonates well with our target audiences. Your understanding of data and markets makes you an invaluable asset to our dynamic team. Using proprietary and open-source software, you automate the retrieval, parsing, and classification of content from various sources, contributing to the improvement of Bloomberg's services. As we maintain our existing infrastructure, your role is central to our transition to models-based classification techniques.

We'll trust you to:

Collaborate with AI Engineering and Data Product teams to enhance news classification model features (Entity, Entity-Linking, Salience) Offer consultation to other data product teams on HITL annotation workflow adaptation for their processes Design a persistent annotation workflow and strategize quality assessment methods with AI Engineering and News Core Product teams Launch news functionalities bespoke for global users and identify unsupported functions for prioritization Collaborate with News Strategy, Sales, and News Product teams to decide on expansion strategies for the global markets Construct and implement improvement strategies for models/news functionalities to bridge identified gaps


You'll need to have:
Please note we use years of experience as a guide but we certainly will consider applications from all candidates who are able to demonstrate the skills necessary for the role.

Bachelor's degree in subject areas such as Linguistics, Journalism, Library Science, Mathematics, Statistics, Data Science,puter Science 3+ years of relevant work experience
Knowledge of financial markets and concepts Problem solving skills and attention to detail Strong written and verbalmunication skills Proven understanding of customer service and experience in building strong relationships with partners Project management experience with the ability to deliver on tight deadlines

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.

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

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.