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Generalist Data Scientist title, unspecified seniority, and metro technical demand raise competition.
Strong ML and data engineering skills transfer across industries, while healthcare/forecasting domain expertise adds specificity.
Multiple mandatory technical skills and platform requirements increase applicant filtering despite no explicit years.
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Develop, optimize, and deploy machine learning models focused on time series forecasting and predictive analytics.
Build and maintain data pipelines and warehouses using Python, PySpark, SQL, and cloud platforms like Google Cloud and Palantir Foundry to support scalable data integration and analytics.
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 business goals.
Proficiency in Python, PySpark, and SQL; strong experience with time series forecasting models including ARIMA, Prophet, LSTMs.
Experience with data model optimization, feature engineering, and performance evaluation using ML metrics.
Hands-on experience with cloud computing (Google Cloud) and data integration tools like Boomi, SnapLogic, SSIS, or Palantir.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in large-scale data integration projects and complex ETL/data transformation processes.
Skilled in operating cloud-based ML platforms and automation tools such as AutoAI and Google Colab.
Able to translate complex data science concepts into actionable business insights, indicating strong communication with cross-functional teams.