Data Scientist

Proofpoint
London
3 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist - Gen AI - Remote

Data Scientist - Gen AI - Remote

Data Scientist - Gen AI - Remote

Data Scientist - Gen AI - Remote

Proofpoint

Proofpoint helps protect people, data and brands against cyber attacks. Offering compliance and cybersecurity solutions for email, web, cloud, and more.

It's fun to work in a company where people truly BELIEVE in what they're doing!

We're committed to bringing passion and customer focus to the business.

Corporate Overview
Proofpoint is a leading cybersecurity company protecting organizations’ greatest assets and biggest risks: vulnerabilities in people. With an integrated suite of cloud-based solutions, Proofpoint helps companies around the world stop targeted threats, safeguard their data, and make their users more resilient against cyber-attacks. Leading organizations of all sizes, including more than half of the Fortune 1000, rely on Proofpoint for people-centric security and compliance solutions mitigating their most critical risks across email, the cloud, social media, and the web.
Protection Starts with People.

The Role
Proofpoint is looking for a Data Scientist III to join the Algo team within the Tessian Business Unit. In this role, your key responsibility will be to analyse data, build ML models and improve algorithms at the core of our email threat detection engine that protects some of the largest businesses in the world.

You will collaborate with Product and Engineering teams to identify and solve the most impactful problems applying state-of-the-art machine learning tools and techniques. You'll be surrounded by incredibly smart, talented and friendly colleagues. Engineers at Tessian love to learn from each other and pull together as a team to succeed.

  1. Wrangle and draw meaningful insights from massive amounts of unstructured textual data using the latest tools and technologies like Spark, Iceberg, Athena, AWS SageMaker.
  2. Apply unsupervised learning algorithms across billions of email interactions to identify emerging threat patterns.
  3. Work with state-of-the-art machine learning models and techniques like encoder-decoder transformers, LLMs, anomaly detection as well as classical ML models to solve a multifaceted problem.
  4. Engineer new features, train and deploy models to production using AWS SageMaker as the MLOps platform.
  5. Adopt a data-driven approach to identify gaps in our detection algorithms and propose improvements to our models and algorithms.
  6. Promote and drive ML best practices and cultivate an environment of experimentation and learning.
  7. Stay up to date with the latest advancements in machine learning, AI technologies, and incorporate them into our solutions where applicable.

What you bring to the team

  1. Experience contributing to multiple highly impactful machine learning projects with proven results.
  2. Hands-on experience in the NLP domain involving training, fine-tuning and productionising transformer-based models for text classification / text-embeddings (experience with LLMs, generative AI is a plus).
  3. Experience monitoring and maintaining performance of models over time in production considering model/data drifts.
  4. In-depth experience with one or more deep neural network frameworks (e.g. PyTorch, Tensorflow, JAX).
  5. A creative mindset, propensity to care deeply about the impact their team has and to encourage novel ways of critical thinking in their team.

Nice to have

  1. Conceptual understanding of Graph Neural Networks and experience applying GNNs to solve real world problem statements will be a plus.
  2. Experience working on large imbalanced datasets, evaluating and selecting models that work well in production on imbalanced real-world data.

Why Proofpoint
Protecting people is at the heart of our award-winning lineup of cybersecurity solutions, and the people who work here are the key to our success. We’re a customer-focused and a driven-to-win organization with leading-edge products. We are an inclusive, diverse, multinational company that believes in culture fit, but more importantly ‘culture-add’, and we strongly encourage people from all walks of life to apply.

We believe in hiring the best and the brightest to help cultivate our culture of collaboration and appreciation. Apply today and explore your future at Proofpoint!

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.