Data Scientist
Core
Design, build, and operationalize advanced data science and machine learning solutions to support customer experience, demand forecasting, operations optimization, and revenue growth in B2B and Digital Commerce environments.
Role type
Applied machine learning engineer (B2B/Digital Commerce)
Builds
Production-ready predictive and prescriptive models, APIs, and containerized solutions for customer-facing and internal analytics.
Domain
B2B, Retail, Digital Commerce
Deliverable
production ML models
Required skills
Python (Pandas, NumPy, Scikit-learn), Advanced SQL, Classical machine learning algorithms, Feature engineering, Statistical modeling, Time-series analysis, MLOps integration, A/B testing, Causal inference
Preferred skills
Deep learning (LSTM, Transformers), Large Language Models (LLMs), Recommendation systems, Snowflake, Azure cloud services, Data engineering concepts (dbt, streaming), GitHub
Technologies
Python, Pandas, NumPy, Scikit-learn, SQL, Snowflake, Azure, GitHub, CI/CD pipelines
Responsibilities
Design and deploy predictive models for demand forecasting and customer behavior analysis; Transition models from research notebooks to production artifacts; Collaborate with engineering on MLOps pipelines; Conduct statistical analysis and A/B testing to measure business impact; Partner with stakeholders to prioritize data science opportunities.
Seniority
Mid-level, hands-on IC