Senior Data Scientist - Pharma

New
T
Tiger AnalyticsPharma analytics
Workable workplace: remote; Workable locations: San Francisco, California, United States. Los Angeles, California, United StatesFull-TimeSenior
Salary not disclosed
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Job Details

Experience
6 – 8 years in data science or applied ML
Required Skills
PythonSQLMachine LearningMLFlowSparkDatabricksDeep LearningPySpark

Requirements

  • Have 6–8 years of experience in data science or applied ML.
  • Have hands-on NBA, NBC, recommendation, or personalization work in production.
  • Bring strong Databricks experience, including Spark/PySpark, Delta Lake, and MLflow.
  • Have proficiency in Python and SQL.
  • Have a solid grounding in machine learning and deep learning, using PyTorch or TensorFlow.
  • Have reinforcement learning experience, such as bandits, Q-learning/DQN, or PPO.
  • Have experience taking models from prototype to production using MLOps practices, including CI/CD, monitoring, and retraining.
  • Have experience with experimentation, A/B testing, and measuring incrementality.
  • Hold a bachelor's or master's degree in a quantitative field; a master's or PhD is preferred.

Responsibilities

  • Design and deploy NBA/NBC models that select the action, channel, content, and timing for each customer or HCP.
  • Build machine learning, deep learning, and reinforcement learning solutions, including contextual bandits, sequential decision models, and policy-based methods.
  • Develop propensity, response, and uplift models for the decisioning layer.
  • Run offline policy evaluation and online experiments to measure incremental impact.
  • Own Databricks training pipelines, including PySpark feature engineering, MLflow tracking, Unity Catalog lineage, and automated evaluation gates.
  • Design feedback loops that feed outcomes back into model retraining.
  • Apply GenAI/LLMs where useful, including content tagging, insight summarization, and content personalization.
  • Partner with engineering, marketing technology, and business stakeholders, and explain results to non-technical audiences.
  • Mentor junior data scientists and set modeling standards.
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