Data Analyst II

About the Role

In this hands‑on individual contributor role, you will design, validate, and deploy machine‑learning and statistical models for use cases like forecasting, segmentation, anomaly detection, and optimization. You will perform exploratory data analysis, work with large structured datasets from warehouses (e.g., Snowflake, Databricks) and enterprise systems, and ensure data quality and pipeline reliability through collaboration with data engineers and BI teams. • Build, validate, and deploy predictive ML and statistical models. • Conduct exploratory data analysis to uncover business insights. • Process large structured datasets from warehouses and enterprise systems. • Partner with data engineers and BI teams to ensure data quality and pipeline reliability. • Convert business challenges into measurable analytical problems. • Communicate model results to both technical and business audiences. • Monitor model performance and drive continuous improvements. • Document models, assumptions, and methodologies per enterprise standards. • Apply supervised/unsupervised learning, regression, classification, clustering, and time‑series forecasting. • Write efficient SQL queries and work on cloud data platforms such as Snowflake. • Use statistical methods, hypothesis testing, and model evaluation techniques. • Handle noisy, real‑world data and address data‑quality issues.

Key Responsibilities

• ▸model development
• ▸data analysis
• ▸sql queries
• ▸cloud platforms
• ▸model monitoring
• ▸data quality

What You Bring

The role requires strong Python expertise (pandas, numpy, scikit‑learn, statsmodels), solid knowledge of supervised and unsupervised learning, regression, classification, clustering, and time‑series forecasting methods such as ARIMA and Prophet. Proficiency in SQL, experience with cloud data platforms, and a good grasp of statistics, hypothesis testing, and model evaluation are essential, as is the ability to handle noisy real‑world data and communicate insights in a business‑friendly manner. • Proficient in Python (pandas, numpy, scikit‑learn, statsmodels). • Present analytical insights in clear, business‑friendly language.

Requirements

• ▸python
• ▸sql
• ▸cloud
• ▸statistics
• ▸machine learning
• ▸business communication

Work Environment

Office Full-Time