Our community of data experts and technologists think disruptively to provide pragmatic solutions for our clients' most complex challenges. We are curious minds who come together in collaborative and inclusive teams to push boundaries to make a positive impact in the world by harnessing the power of data and artificial intelligence (AI).
We are looking for change makers, opportunity creators, status-quo shakers. If that’s you, what are you waiting for?
Making an impact across data archetypes
Data engineers
are responsible for bringing our clients scalable and robust solutions related to the processes of creating pipelines, platforms, organization, governance and data quality. They have experience in cloud, on-premises technologies and migrations.
Data architects
are responsible for designing reference architectures, covering key aspects of data management, governance, domains, modeling, integration, security, compliance and more. They are responsible for the discovery, roadmap, feasibility study and recommendation of frameworks, practices and tools in the data world to better meet business objectives.
Data scientists
are responsible for identifying business opportunities and how to respond to them through the applied use of data and thus maximizing client results. They play a strategic role both from a technical and business point of view, proposing the use of advanced machine learning techniques along with algorithms and success metrics that will serve in the future to evaluate the results of production models.
ML engineers
are responsible for providing the technical components capable of enabling CD4ML principles such as experiment versioning tools, data repositories, automation mats and integration layers with production environments. They work closely with data scientists, evaluating aspects of scalability and performance for proposed data models.
Data analysts
are responsible for conducting complex analysis, proposing business indicators and generating analytic solutions to support clients in generating business value. They have experience in transforming data into insights through understanding the business and creating automated dashboards for demonstrating results and making decisions.
Life at Thoughtworks as a data professional
Learn more about life at Thoughtworks as a data professional from Clara Brünn, Data Scientist, Ina Iovitoiu, Data Scientist, Inna Zykova, Data Engineer and Javier Molina Sanchez, Lead Data Engineer. From choosing Thoughtworks to what it’s like to work here as a data professional to details of their project work and advice to those thinking of bringing their data skills to Thoughtworks – this is great insight into being a data professional at Thoughtworks.
People you might work with
Principal data scientist, Bucharest
With more than 10 years of experience around data, Ina is Data Scientist Lead Consultant at ThoughtWorks Romania. Her expertise covers various industries from Banking to Telco and Digital.
Her work is based on her deep interest in end-to-end Machine Learning pipelines, models explainability, experiment design, and AI bias and fairness, for a more inclusive future. Passionate about travel, she also enjoys reading and yoga.
Principal data scientist, Berlin
Katharine Jarmul is a Principal Data Scientist at Thoughtworks Germany and author of the recent O'Reilly book Practical Data Privacy. Previously, she has held numerous roles at large companies and startups in the US and Germany, implementing data processing and machine learning systems with a focus on reliability, testability, privacy and security.
She is a passionate and internationally recognized data scientist, programmer, and lecturer. Katharine is also a frequent keynote speaker at international software and AI conferences.
Head of data & AI Services UK
As the Data service line lead for Thoughtworks UK and Europe, Danilo is responsible for building high-performing teams and unique propositions that help our clients win with Data & AI. That covers the full spectrum of our services, from data strategy and governance to building the Data & AI products and platforms that bring the strategy to life.
Danilo is also responsible for driving Thoughtworks' unique approaches, such as Data Mesh and Continuous Delivery for Machine Learning (CD4ML).
Danilo was named by DataIQ as one of the most influential people in data in 2022, and our work with ITV using Data Mesh has won the DataIQ ‘Shared data, shared culture’ award in 2022.
How we help our clients
We help our clients to get greater value from data by creating a clear roadmap that ensures trustworthiness, security and compliance, while making it effortlessly accessible and user-friendly. This way they can take control of their data landscape and empower data consumers through clear governance policies and alignment with business objectives.
We support our clients to put their data into action by enabling business teams to create and consume reliable self-service data products that scale easily and support diverse analytics.
With our help they apply world-class data architecture models such as Data Mesh to bring a product mindset, modern software engineering methods, and people-centric changes to accelerate data delivery.
Our data experts consult our clients in elevating their potential for extraordinary results by automating routine work and augmenting your team’s unique capabilities with people-centric, ethical artificial intelligence (AI) and analytics.
Data should challenge our assumptions and instincts from time to time. And if it doesn't there is something wrong.
Data should challenge our assumptions and instincts from time to time. And if it doesn't there is something wrong.
Anna Lagutina
As data-driven applications become increasingly prevalent, it's important to ensure they meet software best practices and standards. With incorrect analysis, we can fail to understand data, accidentally introduce bugs, have mismatching test sets and real-life data, misinterpret our data analysis and who knows what else can go wrong?
Danilo Sato and Kiran Prakash
In this webinar, Thoughtworks' experts discuss the benefits of using the Data Mesh approach and the disadvantages of a centralized data management system, drawing on real life data projects.