Senior Data Scientist - Pharma
- Compensation
- Not disclosed
- Location
- San Francisco, California, United States; Los Angeles, California, United States
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 6+ years
- Category
- Data science
- Posted
Skills
Must have
- 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)
Full description
About Tiger AnalyticsShape the Future of AI. Transform the World's Leading Businesses.
Tiger Analytics is one of the fastest-growing AI services firms, partnering with some of the world's largest and most influential organizations to redefine how business gets done. We help Fortune 500 companies harness the power of Artificial Intelligence, Generative AI, Data Science, Data Engineering and Advanced Analytics to solve their most complex challenges, unlock new growth opportunities, and create lasting business impact.
We are at the forefront of the AI revolution, helping enterprises move beyond traditional analytics to embrace Generative AI, agentic AI, intelligent automation, and cloud-native data platforms. A global presence across North America, Latin America, Europe, and India
At Tiger Analytics, we don't just help businesses adapt to the future—we help them build it
We are seeking a hands-on, highly skilled Senior Data Scientist to join our advanced analytics and AI team supporting some of our large enterprise clients. In this role, You'll build the decisioning engine behind next best action (NBA) and next best content (NBC) for commercial teams. The work covers modeling, offline evaluation, and production deployment on Databricks.
Key Responsibilities:**
- 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
Requirements
- 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)