Responsibilities:
1. End-to-End Model Development:Partner to design, develop, and validate statistical and machine learning models to address key business questions, from initial data exploration to final analysis.
2. Analytical Thought Leader: Lead by example and inspire others, analysing large, complex datasets to extract meaningful insights and solve business problems.This includes elements of Operations Research, using data to optimize decisions and processes
3. Cross-Functional Problem Solving: Collaborate directly with business units to translate their challenges into data science frameworks. This could include:
4. R&D: Accelerating drug discovery, target identification, clinical trial analysis, and drug repurposing.
5. Manufacturing: Optimising supply chain logistics and improving production yields through predictive quality control and maintenance.
6. Commercial: Enhancing sales forecasting, pricingand promotions optimization, personalizing marketing campaigns, understanding customer behaviour, and surfacing data insights via large language models.
7. Generate Actionable Insights: Go beyond model building to interpret results, synthesize findings, and communicate actionable recommendations to stakeholders at all levels.
8. Data Storytelling: Use data visualization and clear communication to present complex analytical findings in a compelling and understandable narrative.
9. Collaborate on Deployment: Partner with Data, AI and ML Engineers to ensure that your models are successfully integrated into business processes and applications.
10. Drive Innovation: Continuously research and apply new methodologies in machine learning, statistics, and AI to keep Elanco at the forefront of data science.
11. Champion a Data-Driven Culture: Promote a data-driven culture within Elanco by educating and mentoring colleagues on data science principles and best practices.
What You Need to Succeed (minimum qualifications):
12. Educational Background: A Master’s or PhD in a quantitative field such as Data Science, Statistics, Computer Science, Operations Research, or a related discipline.
13. Programming Proficiency: Strong programming skills in Python or R, with expertise in data manipulation and machine learning libraries.
14. Statistical Rigor: A deep understanding of statistical principles and experimental design, including hypothesis testing, regression, and classification.
15. Machine Learning Experience: Proven experience applying a range of machine learning techniques (, gradient boosting, clustering, NLP, time-series forecasting) to real-world problems.
16. Data Visualisation and Communication:Expertise in using visualisation tools to create compelling stories and the ability to explain complex topics to a non-technical audience.
17. Business Acumen: A strong ability to grasp business challenges quickly and a passion for connecting data-driven insights to strategic goals. Experience in pharma, manufacturing, or commercial analytics is a major plus.
18. Industry Experience:A deep understanding of life science, covering the business model, regulatory/compliance requirements, risks and rewards. An ability to identify and execute against opportunities within data science that directly support life science outcomes.
19. Database Skills:Proficiency in SQL for querying and extracting data from relational databases.
20. Cloud Environment Familiarity: Experience working with Public Cloud, specifically Microsoft Azure and Google Cloud Platform (GCP) and their associated data and analytics services is highly desirable.
Additional Information:
21. Travel:0-10%
22. Location: Hook, UK - Hybrid Work Environment