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Generalist Data Scientist role with broad, mid-level skills increases candidate competition.
Core ML and data engineering skills are transferable, but Palantir and healthcare forecasting raise domain specificity.
Several mandatory technical stacks (PySpark, GCP, Palantir, time-series, ETL) create stringent filtering for hires.
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Develop and optimize machine learning models focusing on time series forecasting and predictive analytics with measurable business impact.
Build and maintain data pipelines, perform feature engineering, and optimize models for accuracy and efficiency using Python, PySpark, SQL, and cloud-based tools.
Lead large-scale data integration and transformation projects using tools like Boomi, SnapLogic, Palantir, and Google Cloud platforms, ensuring alignment with business objectives.
Proficiency in Python, PySpark, SQL, and experience in time series forecasting models (e.g., ARIMA, Prophet, LSTMs).
Hands-on experience with data engineering, ETL, data pipelines, and data transformation including use of Boomi, SnapLogic, SSIS, or Palantir.
Experience with cloud computing platforms, specifically Google Cloud tools like BigQuery and Vertex AI.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in implementing and optimizing machine learning models in production environments with strong grasp of evaluation metrics like MAPE, RMSE, and R².
Skilled in large-scale data integration projects across varied tools and platforms including Palantir Foundry and Google Cloud.
Capable of collaborating with cross-functional teams to translate business needs into ML and data pipeline solutions, demonstrating strong communication of technical concepts to non-technical stakeholders.