Scope:    Accountable for defined area of data science practices

1. Data Science

• Assist and contribute to the development and apply defined set of advanced analysis methodologies to optimize customer acquisition, membership, engagement, conversion, experience and loyalty. This includes framing of business needs, data exploration as well as descriptive, predictive and prescriptive modeling, under the supervision or with the support of a more senior colleague 
• Assist and contribute to the generation of insights to increase the understanding of adidas consumers, exploring their affinities and intents, in order to make the best recommendations to individual consumers through the right digital channel at the right time, under the supervision or with the support of a more senior colleague.
• Continuously seek to improve our methodologies and challenge the status quo, raising the standards for faster delivery
• Help design, improve, and automate the data science platform and our machine learning pipeline to facilitate our data science activities and serve near real time use cases under the supervision or with the support of a more senior colleague

2. Data Science Network & Storytelling

• Help adidas benefit from innovations in the global ecosystem of a defined area within data science, big data, machine learning, artificial intelligence by introducing state of the art techniques
• Present and promote analysis results and insights to colleagues, include those outside of data science, in a way that allows them to understand the value of our work, apply your output, and realize business potential.

3. Segmentation

• Support the development of a defined set of customer segmentations, acquisition and retention strategies, predictive modelling, customer lifetime value metrics and marketing effectiveness measures.

Required Experience - 3+ Years

Must Have:

Python for Data Analysis & ML
Proficient in pandas, numpy, and scikit-learn; able to clean data, perform feature engineering, and build basic ML models in a structured, reproducible manner.

Statistics Fundamentals
Strong understanding of hypothesis testing, distributions, sampling, confidence intervals, and model evaluation metrics, with practical business application.

Machine Learning Core Techniques
Hands-on experience with regression, classification, and clustering; understands appropriate use cases and can interpret results effectively.

Communication & Collaboration
Comfortable working in cross-functional and global environments; able to explain technical concepts to non-technical stakeholders.

Learning Agility & Curiosity
Demonstrates a strong willingness to learn, challenge existing approaches, and adapt to new tools and business problems.

Customer Analytics Exposure
Experience (academic or professional) in segmentation, propensity modeling, CLTV, cohort analysis, or similar consumer-focused use cases.

Good to Have

Visualization Tools
Experience with Python visualization libraries or BI tools to present insights effectively.

MLOps / Operationalization Awareness
Basic understanding of model deployment, monitoring, and maintenance in production environments.