Data Science

Minimum Work Experience (years):
10+ years of experience in Data Science with emphasis on the full lifecycle of Data Science-ML/AI projects.
Within that timeframe, experience is expected in: Python/PySpark, SQL, and relational or NoSQL databases, and cloud resource management.
Key Skills and Competencies:
Experience working with AWS, Azure, or Google Cloud Platform cloud environments.
Experience implementing advanced analytics, ML/AI algorithms (such as:
▪ Statistical Time Series: Exponential Smoothing Models, S/ARIMA;
▪ Machine Learning: Random Forests, Gradient Boosting Methods;
▪ Neural Networks: TiDE (Google), DenseNet & Prophet (FB-Meta);
▪ Foundational Time Series Models: TimesFM (Google), Chronos (AWS) and
▪ mathematical (constrained linear, non-linear and network) optimization models
Proven experience building end-to-end production grade Data & ML/AI pipelines using
PySpark, Python (Pandas/NumPy) and SQL.
Experience working with Git (or similar code management repositories) as a collaboration tool.
Experience with orchestration tools like Databricks, Airflow (or similar tools like Snowflake, Dagster, etc.).
Understanding of Retail/CPG industry business challenges with an emphasis on Supply
Chain, Pricing/Promotions/Markdowns, Inventory Allocation, Assortment Mix Planning,
Size & Pack Optimization, Retail Shrink/Fraud & Anomaly Detection, and Workforce
optimization applications are highly desirable.
Excellent verbal and written communication skills, especially as it relates to technical
communications. Ability to present technical analysis to business stakeholders.
Demonstrated ability to learn new technologies quickly and independently, particularly
as technology in this domain rapidly advances. In this context, a working knowledge of
GenAI/LLMs, Agentic-AI and related frameworks (e.g. LangChain) will be a plus.
Ability to work independently with minimal supervision and achieve stretch goals in a very innovative and fast-paced environment.