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# Senior Data Scientist - Pharma

- **Company:** Tiger Analytics
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $120,000.0 - $140,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Cloud Computing, Continuous Integration, Python (Programming Language), Machine Learning, Automation of Marketing, Tensorflow, Standard Sql, Reinforcement Learning, Feature Store, Feature Engineering, Pytorch, Large Language Models, Apache Spark, Deep Learning, Data Lakes, Pyspark, Machine Learning Operations, Databricks
- **Published:** October 22, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-data-scientist-pharma-san-francisco-ca--65ce0248-b744-406b-b395-be0b4574142f

## About the Role

* 6 - 8 years in data science or applied ML, with hands-on NBA, NBC, recommendation, or personalization work in production
 * Strong Databricks experience (Spark/PySpark, Delta Lake, MLflow; Feature Store or Unity Catalog a plus)
 * Solid grounding in machine learning, deep learning (PyTorch or TensorFlow), and reinforcement learning (e.g., bandits, Q-learning/DQN, PPO)
 * Proficiency in Python and SQL
 * Experience taking models from prototype to production with MLOps practices (CI/CD, monitoring, retraining)
 * Experience with experimentation, A/B testing, and measuring incrementality
 * Bachelor's or Master's in a quantitative field (Master's or PhD preferred)

Preferred (Not Required)
*

 + Life sciences or pharma commercial analytics (omnichannel, HCP/patient engagement, field force, patient journey data)
 + Familiarity with CRM, CDP, or marketing automation platforms
 + Causal inference and uplift modeling
 + Experience with regulated or compliance-constrained data (e.g., HIPAA)
 + Databricks certifications

## Description

* Design and deploy NBA/NBC models that choose the right action, channel, content, and timing for each customer or HCP
 * Build ML, deep learning, and reinforcement learning solutions, including contextual bandits, sequential decision models, and policy-based methods
 * Develop propensity, response, and uplift models that feed the decisioning layer
 * Run offline policy evaluation and online experiments to measure incremental impact
 * Own Databricks training pipelines: PySpark feature engineering, MLflow tracking, Unity Catalog lineage, and automated evaluation gates before models reach production
 * Design feedback loops so outcomes flow back into retraining
 * Apply GenAI/LLMs where useful, such as 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