Chief Data Scientist

Avanade
Birmingham
1 year ago
Applications closed

Related Jobs

View all jobs

Data Architect

Our AI team is made up of academically accomplished, industry-recognised experts - and there’s room for more analytical, innovative, client-driven data professionals to join us. If you’re passionate about helping clients realise the true value and impact of AI and data science, keep reading. An opportunity on Avanade’s AI team will help take your career to the next level where you can truly do what matters. 

As the technical lead in the AI team, you’ll work across Avanade's Data & AI practice to build, deploy, and scale bespoke, advanced AI solutions and platforms for some of the world's leading businesses. Alongside this, our teams work across a range of industries building solutions at the cutting edge of GenAI, Agentic AI, Computer Vision, IoT, ML, AI, and Deep and Reinforcement Learning. Join our team and you can help our clients realize the potential of their data. Awareness of channels to market and how to work in a Microsoft and partner eco-system to ensure Customer Success. High energy intent to help clients understand the fast moving AI industry, product and service evolution. Experience working with stakeholders to create technical visions, high-level architectures, and delivery roadmaps for large programs of work that involve the latest AI concepts particularly focused on scaling AI across enterprises.Demonstrate a deep understanding of responsible AI principles, practices and regulatory frameworks and articulate clearly how to address risks whilst quickly delivering impactful solutions.Capability in creating design artifacts and presenting them for approval at architectural forums attended by multi-disciplined stakeholders from engineering to senior ("C level") client executives In-depth understanding of modern AI capabilities, structured and unstructured data analysis, streaming data, IoT, AI, GenAI and related topical analytics fieldsSignificant experience in implementing/deploying data science solutions in Azure/AWS/GCPComfortable actively engaging and leading sales and business development opportunities with clientsCharacteristics that can spell success for this roleYou likely possess an advanced degree in a quantitative field such as computer science, applied mathematics, theoretical physics, computational biology, statistics, or machine learning. At Avanade we also support an equivalent combination of education and experience. Proven success in consultative/complex technical sales and deployment projects, architecture, design, implementation, and/or support of highly distributed applications required Extensive experience working with large complex enterprise accounts architecting cloud solutions for data estate workloads and implementing large scale data projects within the cloud environment You are acknowledged for driving decisions collaboratively, resolving conflicts and ensuring follow through with exceptional verbal and written communication skills. Ability to orchestrate, lead, and influence remote teams, ensuring successful implementation of customer projects Consultative, collaborative, relationship builderResilient, adaptable, flexibleHumble leader, master negotiator, relationship builderPassionate about tech and engaging with and advising clientsAs an architect assigned to a client project, you will typically work closely with delivery teams and client stakeholders to provide technical leadership, shape deals and propositions, drive architectural decisions, create plans and roadmaps, and produce design artifacts Play a key role in Avanade's sales process with the responsibility for shaping the high-level design, estimates, and contractual details for client proposals in collaboration with other subject matter experts in the business As a senior member of the Avanade technical community, you will provide coaching and mentoring to motivate and inspire more junior colleagues from within your team and across the organization Have the ability to translate complex technical concepts and solutions and present them in easy to understand language and value based outcomes, to senior business stakeholders, technology leaders and internal sales teams.Engage in early conversations with clients and/or sales team and support origination and deal qualification, requirements capture and solutioning, recommending best practices and approaches to achieve optimal outcomes, building strong relationship with clients to become the trusted advisor. Prepare and represent the solution in Deal Reviews and play an active role in client presentations, and negotiations with key client buyers Work collaboratively with the wider Data & AI leadership to shape our current and future offerings, accelerators, and background IP to support GTM activities Drive the evolution of best practices and methodologies for AI development, including data management, model training, fine-tuning, and deployment.Lead the development of GenAI, Agentic AI, AI platforms, ML, AI, and statistical algorithms and solutions using Python and tools from Microsoft and DatabricksStay abreast of the latest advancements in AI and machine learning technologies and incorporate relevant innovations into our solutions

Some of the best things about working at Avanade

Opportunity to work for Microsoft’s Global Alliance Partner of the Year, with exceptional development and training (minimum 80 hours per year for training and paid certifications) Real-time access to technical and skilled resources globally Dedicated career advisor to encourage your growth Engaged and helpful coworkers genuinely interested in you

Find out more about some of our benefits Employee Benefits at Avanade | Avanade



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.

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.

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.

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.