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- Tasks: Lead data science teams to drive impactful analytics and AI solutions.
- Company: Join a leading firm in a highly regulated, data-driven environment.
- Benefits: Competitive salary, bonuses, equity options, and professional growth opportunities.
- Why this job: Shape the future of data science while making a real business impact.
- Qualifications: 8-12 years in data science with proven leadership in regulated settings.
- Other info: Collaborative culture with a focus on innovation and ethical practices.
The predicted salary is between 72000 - 108000 £ per year.
This is a senior leadership role responsible for defining and executing a data science strategy within a highly regulated, data-driven environment. You will lead multiple data science and machine learning teams, partnering closely with Product, Engineering, Risk, and Compliance to deliver measurable business outcomes through advanced analytics and AI. The role combines hands-on technical leadership with strategic ownership, stakeholder influence, and people development.
Key Responsibilities:- Define and own the data science vision, roadmap, and operating model aligned to business objectives
- Build, lead, and scale high-performing teams of data scientists and ML engineers
- Establish best practices across experimentation, model development, validation, and deployment
- Act as a senior advisor to Product, Risk, and Executive stakeholders
- Lead development of predictive models, decisioning systems, and optimisation solutions
- Drive use cases across areas such as credit risk, fraud, pricing, customer analytics, forecasting, and personalisation
- Ensure models are production-ready, scalable, explainable, and continuously monitored
- Translate complex analytical insights into clear, actionable recommendations
- Ensure data science practices meet regulatory, ethical, and governance standards
- Partner with Risk, Legal, and Compliance on model governance and regulatory engagement
- Own model documentation, validation frameworks, and performance reporting
- Work closely with Data Engineering and Platform teams to ensure robust pipelines and tooling
- Partner with Product teams to embed data science into customer-facing features
- Collaborate with Engineering to operationalise models using modern MLOps practices
- Experience: 8–12+ years in data science, analytics, or applied machine learning; proven experience leading and scaling data science teams in regulated environments; strong track record of delivering production ML models with measurable business impact
- Technical Skills: Deep grounding in statistics, machine learning, and predictive modelling; strong proficiency in Python (and/or R) and SQL; experience with cloud platforms (AWS, GCP, or Azure); familiarity with MLOps, deployment patterns, and model monitoring; experience working with large, complex, and imperfect datasets
- Leadership & Communication: Strong people leadership, mentoring, and coaching capability; ability to influence senior stakeholders and explain complex concepts clearly; commercial mindset with a focus on outcomes and value creation
- Experience in credit risk, fraud, AML, payments, or trading
- Exposure to real-time or near-real-time decisioning systems
- Experience supporting regulatory reviews or audits
Head of Data Science employer: W Talent
Contact Detail:
W Talent Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Head of Data Science
✨Tip Number 1
Network like a pro! Reach out to your connections in the data science field and let them know you're on the hunt for a Head of Data Science role. Personal recommendations can make all the difference, so don’t be shy about asking for introductions.
✨Tip Number 2
Show off your expertise! Attend industry meetups or webinars and share your insights. This not only boosts your visibility but also positions you as a thought leader in data science, making you more attractive to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and leadership experiences. Be ready to discuss how you've led teams and delivered impactful ML models. We want to see your passion for data science shine through!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive and engaged with our platform.
We think you need these skills to ace Head of Data Science
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the specific skills and experiences mentioned in the job description. Highlight your leadership experience in data science and any relevant projects that showcase your ability to deliver measurable business outcomes.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're the perfect fit for this role. Share your vision for data science and how you plan to lead teams to success. Don't forget to mention your experience with regulatory environments and stakeholder engagement!
Showcase Your Technical Skills: We want to see your technical prowess! Include specific examples of predictive models you've developed and the impact they've had. Mention your proficiency in Python, SQL, and any cloud platforms you've worked with to demonstrate your hands-on experience.
Apply Through Our Website: For the best chance of getting noticed, make sure to apply through our website. This helps us keep track of your application and ensures it reaches the right people. We can't wait to see what you bring to the table!
How to prepare for a job interview at W Talent
✨Know Your Data Science Vision
Before the interview, take some time to clearly define your vision for data science in a regulated environment. Be ready to discuss how you would align this vision with business objectives and what strategies you would implement to achieve measurable outcomes.
✨Showcase Your Leadership Skills
Prepare examples of how you've successfully built and led high-performing data science teams. Highlight your mentoring and coaching experiences, and be ready to discuss how you influence senior stakeholders and foster collaboration across departments.
✨Demonstrate Technical Proficiency
Brush up on your technical skills, especially in Python, SQL, and machine learning. Be prepared to discuss specific projects where you've delivered production ML models and how you ensured they were scalable and explainable.
✨Understand Governance and Compliance
Familiarise yourself with the regulatory standards relevant to data science practices. Be ready to talk about your experience with model governance, documentation, and how you've partnered with legal and compliance teams to meet these standards.
