Job Summary/Purpose:
As a Data Scientist at, you will play a critical role in advancing this FTSE business data capabilities, applying advanced analytical methods and machine learning techniques to solve core business problems. The appetite at this organisation is huge, and financially backed. Your work will enable data-driven decision-making and support the transition to a more data-centric approach. This role involves collaborating closely with Business Partnering and Enterprise Architecture teams to understand business needs and deliver tailored data science solutions that drive strategic value. You will help build a robust data science function, focusing on developing models, analyzing data, and delivering insights that support the businesses growth and transformation.
Primary Responsibilities:
Data Science & Advanced Analytics:
* Develop and implement predictive models and algorithms to solve business challenges, including regression, classification, clustering, and time-series forecasting.
* Analyse complex datasets to uncover trends, patterns, and insights, translating them into actionable recommendations for stakeholders.
* Apply machine learning techniques and statistical analysis to drive data-driven solutions, optimizing business processes and decision-making.
* Design, develop, and deploy data models and pipelines, ensuring seamless integration into existing systems.
Stakeholder Collaboration:
* Work closely with Business Partnering to translate business needs into data science requirements, ensuring alignment with strategic goals.
* Act as a trusted advisor, presenting complex findings in a clear and accessible manner to technical and non-technical stakeholders.
* Partner with product and commercial teams to integrate data science models into data products, enhancing value for companies customers.
Model Development & Deployment:
* Develop and deploy machine learning models, ensuring their accuracy, scalability, and alignment with business objectives.
* Implement model monitoring and evaluation techniques, such as A/B testing, to validate model performance and refine as necessary.
* Maintain thorough documentation of data science processes, models, and outcomes to ensure transparency and reproducibility.
Continuous Improvement & Innovation:
* Stay up-to-date with the latest advancements in data science, machine learning, and AI to bring cutting-edge methods to the organization.
* Identify opportunities for innovation and continuous improvement in the data science function, recommending new tools and techniques.
* Foster a culture of experimentation and learning within the team, encouraging the exploration of new methodologies and technologies.
Risk Management & Compliance:
* Identify potential risks associated with data science projects, ensuring models are compliant with internal and external data governance and security standards.
* Collaborate with the Head of Data & Analytics and D&A Manager to mitigate risks and communicate potential impacts to the organization.
Key Business Relationships:
The Data Scientist will be responsible for managing relationships with Data & Analytics stakeholders, business stakeholders, and Heads of Function. This role will also work closely with:
* Business Partnering: to ensure that all data science solutions are aligned with business needs and goals.
* Project & Transformation Teams: to integrate data science models into broader business transformation initiatives.
* Enterprise Architecture & IT: to ensure data models align with the company's data architecture and technological standards.
Measures of Success:
* Model Performance: High accuracy and robustness of deployed models, demonstrated through improved decision-making and business outcomes.
* Insight Generation: Proven ability to generate actionable insights that drive strategic business objectives and deliver measurable value.
* Stakeholder Satisfaction: Positive feedback from stakeholders on the clarity and impact of data science insights.
* Continuous Improvement: Adoption of innovative data science techniques and tools that enhance the organization's data capabilities.
Experience, Qualifications, Technical Requirements:
* Demonstrable experience in data science, advanced analytics, or a related field, with a focus on building and deploying predictive models.
* Strong proficiency in programming languages such as Python or R, and experience with libraries like scikit-learn, TensorFlow, or PyTorch.
* Hands-on experience with data visualization tools (e.g., Power BI, Tableau) and the ability to present complex data in an accessible way.
* Ability to work collaboratively with cross-functional teams and communicate complex technical concepts to non-technical stakeholders.
* Problem-solving skills and a proactive approach to identifying opportunities for applying data science to business challenges.
Key Competencies:
* Collaboration & Communication: Build strong relationships across functions and communicate data insights clearly.
* Innovative Thinking: Continuously explore new methodologies and techniques in data science.
* Strategic Impact: Focus on delivering solutions that align with business goals and drive value.
* Technical Excellence: Demonstrate a strong understanding of data science principles and best practices.