Applied Data Scientist

Tadaweb
London
8 months ago
Applications closed

Related Jobs

View all jobs

Applied Data Scientist: Predictive ML for Risk & Growth

AI Data Scientist: Applied Intelligence & Delivery

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Tadaweb is a pioneering technology company with roots in Luxembourg and a growing global presence, with offices in the United Kingdom, France, and the United States. For over 13 years, we’ve been on a mission to make the world a safer place by empowering analysts with the tools they need to access the right information at the right time. Our cutting-edge SaaS platform revolutionizes PAI and OSINT investigations, making them faster, smarter, and more effective, all while adhering to the highest ethical standards by relying solely on publicly available information and supporting our clients’ policies. Renowned for our “nothing is impossible” ethos, we prioritize trust, transparency, and innovation in everything we do.


We are looking for a skilledData Scientistto help us unlock the power of our data and take our platform into the future. As part of our team, you will play a key role in analyzing our data and designing models that enhance our AI-powered analytics capabilities. You'll work alongside our skilled team of engineers and machine learning experts, directly contributing to next-generation capabilities that enable our users to automate their PAI collection and analysis workflows. This role is crucial to our mission of making the world safer by empowering the human mind with the right information at the right time.


Scope of Work:

  • Design and improve NLP models for text analysis, entity recognition, and relationship extraction from web content
  • Analyze data to identify patterns and insights that can enhance our platform's capabilities
  • Research and evaluate algorithms for information discovery from publicly available sources
  • Design advanced search and recommendation capabilities
  • Create and refine machine learning models for risk identification and pattern detection
  • Collaborate with engineers to integrate your data science solutions into our platform
  • Design and analyze A/B tests to measure the effectiveness of new features and models
  • Develop data visualization dashboards to communicate insights to users and stakeholders
  • Apply responsible AI practices in all aspects of model development
  • Analyze model performance, diagnose issues, and recommend improvements
  • Research and evaluate emerging technologies in NLP, information retrieval, and ML
  • Document methodologies, model architectures, and experimental results
  • Mentor junior team members and contribute to the data science community of practice


Your Profile:

  • Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related fields (PhD a plus)
  • Strong programming skills in Python, with proficiency in data science libraries (NumPy, Pandas, scikit-learn)
  • Experience with SQL for data querying and analysis
  • Solid understanding of machine learning algorithms, statistical methods, and predictive modeling
  • Experience with NLP techniques for text analysis, classification, and information extraction
  • Knowledge of deep learning frameworks such as PyTorch or TensorFlow
  • Experience with data visualization tools (Matplotlib, Seaborn, Plotly, or similar)
  • Strong analytical mindset with a focus on solving real-world problems
  • Excellent communication skills to present findings to technical and non-technical stakeholders
  • Fluent in English


You get bonus points if you have any of the following:

  • Experience with large language models and generative AI technologies
  • Background in information retrieval systems or search engine development
  • Experience with OSINT or intelligence analysis tools and methodologies
  • Understanding of data privacy regulations and security considerations
  • Experience with time series analysis and anomaly detection
  • Familiarity with A/B testing and experimentation design
  • Experience working with PAI sources and analytical techniques
  • Knowledge of graph databases and network analysis for relationship mapping
  • Open-source contributions or research publications in relevant fields
  • Familiarity with cloud platforms (AWS, Azure, or GCP) for ML model deployment
  • Knowledge of MLOps practices and tools for experiment tracking
  • Experience with big data processing frameworks like Apache Spark
  • Knowledge of NoSQL databases (MongoDB, Elasticsearch) for handling unstructured data
  • Experience with data versioning and feature stores for machine learning
  • Proficiency in model deployment using containerization (Docker, Kubernetes)


What we offer:

  • The opportunity to join a growing tech company, with strong product-market fit and an ambitious roadmap
  • The chance to join a human-focused company that genuinely cares about its employees and core values and with an international exposure with our global team across Luxembourg, Paris, London, and Washington DC
  • A focus on performance of the team, not hours at the desk.
  • A social calendar including family parties, games nights, annual offsites, End of the year events and more, with an inclusive approach for both younger professionals and parents.


Tadaweb is an equal opportunity employer and we strive to have a team with diverse perspectives, experiences and backgrounds.


Our culture:

  • Our company culture is driven by the core values of family first, nothing is impossible and work hard, play harder.
  • We provide a healthy and positive culture that cares about employee wellbeing by creating a great workplace and investing our employees learning and development.
  • Our leaders aspire to the philosophies of extreme ownership, and servant leadership.


#engineering

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.