Principal Product Designer AI Specialist

Datasite
1 year ago
Applications closed

Related Jobs

View all jobs

GCP Data Architect

GCP Data Architect

Principal Data Engineer

Principal Data Engineer

Data Architect

Data Analyst - Farming Operations

Job Description:

We are looking for an experiencedAIProductDesignerto join our distributed team, building solutionsthat truly resonate with ourglobalcustomerbase.As a key contributor, you will be detail-oriented, but also capable of seeing the bigger picture. This is an opportunity to innovate in a complex space and will suit someone with a passion forProduct Led Growth (PLG),experimentation,evidence-based decision-makingand building greatuserexperiences.You will work alongside a dedicated Product Managerwith a cross-functional teamincludingAI/ML scientists and engineersto leadGenAIinitiatives across theDatasiteplatform.

Responsibilities:

  • UX Design:Lead and execute design projects from concept through completion, ensuringhigh standardsof design quality and user experience.Workwith product teams to understand user needs and business goals, translating complex requirements into clear, customer-centric design solutions.

  • Strategy:Drive theGenAIdesign vision and strategy across the platform, advocating for user-centered design practices and ensuring design consistency and coherence throughout all product touch points.

  • Leadership:Elevate the design team by providingGenAIguidance on best practices, design processes, and innovative approaches.

  • Trust:Maintainand develop customer trust while enhancing the usability and functionality of AI-powered features.

  • User Research:Conductresearch at all stages of the product lifecycle, from early strategic discovery through tousability testing and gatheringuser feedback to continuously improve the product experience.

  • Product Led Growth (PLG):Implement andchampionProduct Led Growth strategies, fostering a user-centric approach that empowers users to discover, adopt, and derive value from the product independently.

  • Cross-Functional Collaboration:Leadand work closely withacross-functional teamthrough activeparticipationin stand-ups and other Agile ceremonies,fostering collaboration and ensuring alignmentrequiredto deliver successful product outcomes.

  • Security & Compliance:Maintaina security-first mindset in the product development process, ensuring robust safeguards, data protection, and compliance with legislation and standards.

  • Market Analysis:Perform research to stay informed about industry trends, emerging technologies, and competitors, using these insights to keep our products competitiveand at the forefront of innovation.

Requirements:

  • 8+ years' experience as aUX/Product Designer

  • Proven experienceresearching,designing, anditerating post-launch onGenAIproductsor features(Experience with AI Search preferred)

  • In-depth knowledge of how generative AI models work, including Natural Language Processing (NLP), image generation, and other AI-driven designtools

  • Familiarity with AI tools and platforms such asOpenAI'sGPT, DALL-E,Midjourney, or other generative AI frameworks

  • Understandingofthe ethical implications of AI, including issues of bias, privacy, and transparency

  • Skills in identifying and mitigating biases in AI models to ensure fair and inclusive userexperiences

  • Experience with APIs and integrating AI services into digitalproduct

  • History of working with and contributing to a Design System

  • Solid understanding of UX Laws and visual design principles

  • A passion for speed, quality, and evidence-based learning and decision-making

  • Demonstrated experience with enterprise products in complex domains such as financial services,healthcare, andeducation

  • Technical aptitude and attention to detail,while always keepingthe bigger picturein sight

  • Exceptional strategic thinking abilities, plusa demonstratedcapacityto convert strategy into actionabletacticalplans

  • Ability toanalyzedata to understand userbehaviorand improve AImodels

  • Knowledge of statistical analysis and data visualization tools

  • Strong problem-solving skills, with a results-oriented approach to overcoming challenges

  • Strong interpersonal, communication, and presentation skills, with the ability toevangelize,influence, engage, and collaborate effectively across various teams and levels of the organization, as well as customers andpartners

  • A demonstrated ability to thrive in a fast-paced, dynamic environment and manage multiple prioritieseffectively

  • Experience working with cross-functional Agile teams in remote / hybridenvironments

  • Experience with software such as Figma, Dovetail,Pendo, and other BI (Business Intelligence) tools, as well as productivity / collaboration tools such asProductboard, Monday, JIRA, Slack, Mural, Office 365 etc.

  • Self-starter and quick learner

  • A passion for speed,quality,and evidence-based learningand decision-making

As a global organization, Datasite knows that diverse perspectives are essential to our success. We’re committed to maintaining a diverse workforce to serve our customers around the world. Datasite is an equal opportunity employer (EEO) and furthers the principles of EEO through Affirmative Action.

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.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.