Creates, employs, evaluates, optimizes, and maintains generative AI, machine learning, time series forecasting, mathematical optimization, simulation, and NLP models and workflows that deliver actionable insights and meet business objectives.
Participates in the development, delivery, and maintenance of a Generative AI Platform by implementing and proposing new features, designing evaluation experiments, and researching the latest advancements in the GenAI field.
Extracts valuable information from large structured and unstructured datasets, identifying key variables and metrics that influence consumer behavior, commercial activities, product supply, R&D and RMSQC.
Builds prototypes to validate modeling concepts.
Industrializes and scales up successful advanced analytics prototypes into IT products.
Creates intuitive and interactive data visualizations, reporting tools and dashboards to communicate complex analytical findings to both technical and non‑technical stakeholders, and to support scenario creation and management.
Develops generalized frameworks and libraries for repetitive data science activities.
Participates in exploratory projects that investigate new data sources, tools, and analytical techniques to keep Bayer at the forefront of data science in the consumer health sector.
Builds data processing pipelines, analyzes data used for modeling, and performs other data‑related activities as required.
Manages code in GitHub repositories and performs peer code reviews.
Fosters a culture of continuous learning within the data science team by actively participating in workshops and training sessions to share knowledge and skills.
Presents compelling data‑driven stories to all levels of the organization—including peers, senior management, and internal customers—to drive both strategic and operational business decisions.
Acts as a subject matter expert in data science, advising on best practices and emerging technologies that can enhance Bayer Consumer Health’s data science capabilities.
WHO YOU ARE
Master’s or PhD degree with 3+ years of experience in Data Science or related fields.
Proven educational background or applied experience in at least one of the following: Machine Learning, Statistics, Mathematics, Computer Science, Quantitative Finance/Economics/Marketing, Biostatistics, Bioinformatics, or other quantitative disciplines.
Good proficiency and practical experience in machine learning and generative AI.
Good knowledge of data science tools, libraries, frameworks, and platforms such as Python, SQL, AWS (Sagemaker, Bedrock, S3, Glue, Athena), Snowflake, GitHub, PostgreSQL, pgvector, Pandas, NumPy, Scikit‑learn, LangChain, LangGraph, LangFuse, LightRAG.
Ability to write production‑grade code using object‑oriented programming principles.
Ability to design and conduct experiments for building well‑performing agentic AI systems and evaluating user experience, system bottlenecks, and model performance.
Experience developing advanced analytics and AI products in a cloud environment and delivering valuable analysis through the application of domain and business knowledge.
Strong problem‑solving and analytical skills.
Strong interpersonal and communication skills including active listening, consulting, challenging, and presentation skills.