Description
Key Responsibilities May Include :
- Collaborate with key stakeholders to identify business challenges, translating ambiguous problems into structured analyses using statistical modelling and machine learning algorithms.
- Lead the selection, validation, and optimization of models to discover meaningful patterns and insights, ensuring models remain relevant, reliable, and scalable.
- Drive continuous integration and deployment of data science solutions, optimizing performance through advanced machine learning techniques, code reviews, and best practices.
- Develop and deliver sophisticated visualizations, dashboards, and reports translate complex data into clear, actionable insights for business stakeholders.
- Present technical solutions to business stakeholders, using creative methods to explain complex concepts, increase understanding, and encourage solution adoption.
- Mentor and develop junior data scientists, fostering a culture of continuous learning, knowledge sharing, and skills development within the organization.
- Write clean, high-quality code, ensuring all outputs pass quality assurance checks, and contribute to the development of novel solutions to solve complex business problems.
- Stay informed on industry trends, emerging tools, and techniques, applying them to improve data science practices and encourage innovation within the team.
- Lead strategy development for one or more data products, managing roadmaps, identifying requirements, and collaborating with business stakeholders to ensure alignment with business goals.
Sr. Data Scientist –Any of the location - London / Manchester / Madrid (1 Position)
Position Purpose
The Senior Data Scientist is responsible for designing and developing advanced tools and products that leverage Machine Learning, Data Science, and Generative AI techniques using data sourced from various internal and external platforms. This role focuses on increasing supply chain efficiency, boosting productivity, and delivering measurable value to customers by implementing innovative models, algorithms, and data-driven solutions aligned with business goals.
Major / Key Accountabilities
- Design, develop, and deploy machine learning models, algorithms, and advanced analytics solutions to improve supply chain efficiency, productivity, and decision-making.
- Leverage data from multiple internal and external sources to build innovative tools and data products that deliver measurable business value.
- Collaborate closely with cross-functional teams including data engineers, product managers, and business stakeholders to align analytics solutions with strategic objectives.
- Ensure data quality, model reliability, and performance by validating datasets and monitoring deployed models.
- Lead and mentor junior data scientists and analysts, fostering skill development and best practices within the team.
- Drive continuous innovation by exploring emerging data science and AI technologies, including generative AI for supply chain applications.
- Communicate insights, risks, and recommendations effectively to both technical and non-technical stakeholders.
- Support prioritization and management of data science workstreams to meet delivery timelines and resource allocation.
- Contribute to the creation of business cases by quantifying the impact of data science solutions on supply chain KPIs and financial outcomes.
- Focus on data science modelling in close collaboration with the Data Engineering team, which is responsible for data wrangling, clean-up, and transformation to provide high-quality data for analysis.
Experience
- Proven track record designing, developing, and deploying advanced machine learning and statistical models in complex supply chain environments.
- Extensive hands‑on experience collaborating with data engineering teams for data wrangling, cleaning, and transformation to ensure high-quality datasets for modelling.
- Proficient in programming languages such as Python, R, and SQL for data analysis and model development.
- Experience working with cloud computing platforms including AWS and Azure, and familiarity with distributed computing frameworks like Hadoop and Spark.
- Deep understanding of supply chain operations and the ability to apply data science methods to solve real‑world business problems effectively.
- Strong foundational knowledge in mathematics and statistics, typically to at least MSc level, enabling rigorous analytical modelling.
- Demonstrated success driving cross‑functional collaboration with product managers, engineers, and business stakeholders to deliver impactful, user‑centric data products.
- Good presentation and communication skills, capable of translating complex analytical concepts to diverse audiences including non‑technical stakeholders.
- Experience mentoring junior data scientists and fostering a culture of continuous innovation and best practice adoption.
- Skilled in balancing urgent delivery demands with long‑term strategic planning, including supporting business case development and resource prioritization.
Skills & Knowledge
- Demonstrable experience with machine learning techniques and algorithms, with a strong track record of deploying models that serve real users at scale without incurring technical debt.
- Proficiency in statistical methods and experience following CRISP‑DM data science lifecycle.
- Expertise taking projects from ideation or experimental Jupyter notebooks to full production deployment.
- Strong programming skills in Python, with familiarity in ML libraries / frameworks such as TensorFlow, PyTorch, and Scikit‑learn.
- Experience with MLOps practices including model drift detection, decay, A / B testing, integration testing, differential testing, Python package building, and code version control.
- Skilled in data pipeline creation and working with both structured and unstructured data.
- Familiar with cloud platforms (AWS, Azure, GCP) and containerization technologies like Docker and Kubernetes.
- Excellent problem‑solving skills, combined with the ability to communicate complex technical concepts clearly to non‑technical stakeholders.
- Ability to mentor and lead a team of data scientists, machine learning engineers, and data engineers, with strategic decision‑making capability.
Essential Qualifications
- Degree in Data Science, Computer Science, Engineering, Science, Information Systems and / or equivalent
- formal training plus work experience
- BS & 5+ years of work experience
- MS & 4+ years of work experience
- Proficient with machine learning and statistics
- Proficient with Python, deep learning frameworks, Computer Vision, Spark
- Have produced production level algorithms
- Proficient in researching, developing, synthesizing new algorithms and techniques
- Excellent communication skills
Desirable Qualifications
- Master’s or PhD level degree
- 5+ years of work experience in a data science role
- Proficient with cloud computing environments, Kubernetes, etc.
- Familiarity with Data Science software & platforms (e.g. Databricks)
- Software development experience
- Research and new algorithm development experience
Remote Type
Hybrid Remote
Skills to succeed in the role
Adaptabilité, Apprentissage actif, Apprentissage machine, Bitbucket, Cloud Infrastructure (Aws), Curiosity, Empathie, Git, Initiative, Intelligence émotionnelle, Interprétation de données, Littératie numérique, Outils SQL, Plateforme Databricks, Python (langage de programmation), Résolution de problème, Révisions de code, Science des données, Travail interfonctionnel
We are an Equal Opportunity Employer, and we are committed to developing a diverse workforce in which everyone is treated fairly, with respect, and has the opportunity to contribute to business success while realizing his or her potential. This means harnessing the unique skills and experience that each individual brings and we do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state, or local protected class.
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