Back
Broad, mid-level ML Data Scientist role with common title and metro hiring, increasing applicant competition.
Core ML and data engineering skills are transferable, but healthcare and supply-chain forecasting add moderate domain bias.
Extensive mandatory ML, data engineering, cloud, and tooling requirements make screening stringent.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop, optimize, and deploy machine learning models focused on time series forecasting and predictive analytics to drive measurable business value.
Build and maintain large-scale data pipelines and data warehouse solutions using PySpark, SQL, cloud platforms, and ETL tools for seamless data integration and transformation.
Continuously evaluate and improve model performance using metrics such as MAPE, RMSE, and R², collaborating with business stakeholders to align models with user expectations and objectives.
Proficiency in Python, PySpark, and SQL with experience in time series forecasting (ARIMA, Prophet, LSTMs).
Experience in data engineering including data pipelines, ETL, and data transformation using tools like Boomi, SnapLogic, SSIS, or Palantir.
Hands-on knowledge of cloud platforms, particularly Google Cloud (BigQuery, Vertex AI, Cloud Functions).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced with large-scale data integration projects and data model optimization to deliver high accuracy ML solutions in production environments.
Familiar with automation and collaborative ML tools such as AutoAI, Google Colab, and Palantir Foundry for data processing and model automation.
Background or interest in domains like supply chain, logistics, or operational forecasting with MLOps and batch/real-time processing experience.