Senior Data Scientist - Pharma

Tiger Analytics • San Francisco, CA

1d

About The Position

Tiger Analytics is seeking a hands-on Senior Data Scientist to join its advanced analytics and AI team, supporting large enterprise clients. The role builds decisioning engines for next best action (NBA) and next best content (NBC) for commercial teams, spanning modeling, offline evaluation, and production deployment on Databricks. The position partners with engineering, marketing technology, and business stakeholders, communicates results to non-technical audiences, and mentors junior data scientists while setting modeling standards.

The work includes developing machine learning, deep learning, and reinforcement learning solutions; building propensity, response, and uplift models; evaluating policies and online experiments; and operating Databricks training pipelines. It also involves establishing feedback loops for retraining and applying GenAI or large language models where useful, such as content tagging, insight summarization, and content personalization.

Requirements

• 6–8 years in data science or applied machine learning, with hands-on NBA, NBC, recommendation, or personalization work in production.
• Strong Databricks experience, including Spark/PySpark, Delta Lake, and MLflow.
• 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 such as CI/CD, monitoring, and retraining.
• Experience with experimentation, A/B testing, and measuring incrementality.
• Bachelor's or Master's degree in a quantitative field.

Nice To Haves

• Master's or PhD degree preferred.
• Databricks Feature Store or Unity Catalog experience.
• Life sciences or pharma commercial analytics experience, including omnichannel, HCP/patient engagement, field force, or patient journey data.
• Familiarity with CRM, CDP, or marketing automation platforms.
• Causal inference and uplift modeling experience.
• Experience with regulated or compliance-constrained data, such as HIPAA.
• Databricks certifications.

Responsibilities

• Design and deploy NBA/NBC models that select the right 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 that feed 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 before models reach production.
• Design feedback loops so outcomes flow back into 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.

Benefits

• Medical, dental, and vision coverage.
• Retirement accounts.
• Long- and short-term disability insurance.
• Life insurance.
• HSA/FSA accounts (USA).
• Discretionary time-off.
• Continuous learning, hands-on exposure to emerging technologies, and opportunities to build expertise in Generative AI, agentic AI, and next-generation data platforms.
• Career development in a fast-growing, challenging entrepreneurial environment with a high degree of individual responsibility.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior