Staff Data Scientist

Optimizely
London
3 months ago
Applications closed

Related Jobs

View all jobs

Staff Data Scientist

Staff Data Scientist

Staff Data Scientist

Staff Data Scientist: Vision & Authenticity AI (Equity)

Staff Data Scientist

Staff Data Scientist – Experimentation: Innovation & Research

At Optimizely, we\'re on a mission to help people unlock their digital potential. We do that by reinventing how marketing and product teams work to create and optimize digital experiences across all channels. With Optimizely One, our industry-first operating system for marketers, we offer teams flexibility and choice to build their stack their way with our fully SaaS, fully decoupled, and highly composable solution.

We are proud to help more than 10,000 businesses, including H&M, PayPal, Zoom, and Toyota, enrich their customer lifetime value, increase revenue and grow their brands. Our innovation and excellence have earned us numerous recognitions as a leader by industry analysts such as Gartner, Forrester, and IDC, reinforcing our role as a trailblazer in MarTech.

At our core, we believe work is about more than just numbers -- it\'s about the people. Our culture is dynamic and constantly evolving, shaped by every employee, their actions and their stories. With over 1500 Optimizers spread across 12 global locations, our diverse team embodies the "One Optimizely" spirit, emphasizing collaboration and continuous improvement, while fostering a culture where every voice is heard and valued.

Join us and become part of a company that\'s empowering people to unlock their digital potential!

To get a sneak peek into our culture, find us on Instagram: @optimizely

Overview

Why is this team critical to Optimizely?

Business Impact: Powering Optimizely One, our ML innovations are crucial for defining Optimizely\'s technological leadership in digital experience platform

High Visibility: ML capabilities developed by this team are central to Optimizely\'s strategic product roadmap and cross-product integration

Shape the Future: we are positioned to define the next generation of intelligent digital interaction across global brands

The Role

We are seeking an exceptional Staff Data Scientist who will architect and technically lead projects dedicated to pushing the boundaries of AI-driven digital experiences. In this pivotal role, you will guide the development of cutting-edge machine learning solutions that not only anticipate but actively shape how global brands engage, understand, and delight their customers through intelligent, predictive, and personalized digital interactions.Some Technologies We Work With:Python primarily, with bits and pieces of typescript and scalaGCP, AWS, Azure – in this order of relevanceGitHub, Docker, GitHub Actions, Terraform, KubernetesPandas, PySpark and SparkVertex AI, Azure OpenAI for LLMs

Job Responsibilities
  • Lead the execution of projects in a high-performing data science team, fostering professional growth and creating an inclusive and collaborative environment
  • Architect and design scalable machine learning systems that solve complex challenges
  • Develop and execute the data science team\'s strategic roadmap, managing complex projects from conception to deployment
  • Make high-impact technical decisions and provide guidance
  • Establish best practices in software engineering and machine learning development
  • Implement rigorous code quality and testing standards across data science projects
  • Support talent acquisition and continuous learning initiatives
Knowledge and Experience
  • Knowledge of ML model development and deployment frameworks (MLFlow, Kubeflow
  • Advanced data querying (SQL) and data engineering pipelines (Airflow
  • Extensive experience with comprehensive unit testing, integration testing, and test coverage strategies
  • Experience working with Product Management teams and ability to translate complex technical concepts for non-technical stakeholders
Education

PhD\’s, Master\'s, or Bachelor\'s degree in Computer Science, Machine Learning, or related field

Driving Continuous Improvement

Driving for Results

Driving Projects to Completion

Interacting with People at Different Levels

Communicating Effectively

Prioritizing and Organizing Work

Developing Talent

Displaying Technical Expertise

Accepting Responsibility

Optimizely is committed to a diverse and inclusive workplace. Optimizely is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.


#J-18808-Ljbffr

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.

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.

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

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.