Senior Data Scientist - Pharma

Tiger Analytics Inc.•Los Angeles, CA

1d•$120,000 - $140,000

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 focuses on building the decisioning engine for next best action (NBA) and next best content (NBC) for commercial teams, spanning modeling, offline evaluation, and production deployment on Databricks. The position involves designing and deploying personalization and decisioning models, developing machine learning and reinforcement learning solutions, operating Databricks training pipelines, and building feedback loops that support model retraining. The Senior Data Scientist will apply generative AI where useful, work with engineering, marketing technology, and business stakeholders, communicate results to non-technical audiences, and mentor junior data scientists. Tiger Analytics describes itself as a growing AI services firm that partners with major organizations to address complex business challenges using AI, data science, data engineering, and advanced analytics. The company offers opportunities to work with global teams and develop expertise in emerging AI technologies.

Requirements

6–8 years of experience in data science or applied machine learning.
Hands-on experience with NBA, NBC, recommendation, or personalization work in production.
Strong Databricks experience, including Spark or PySpark, Delta Lake, and MLflow.
Solid grounding in machine learning, deep learning using PyTorch or TensorFlow, and reinforcement learning such as bandits, Q-learning/DQN, or PPO.
Proficiency in Python and SQL.
Experience taking models from prototype to production using MLOps practices, including 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.
Life sciences or pharmaceutical commercial analytics experience, including omnichannel, HCP or 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.
Experience with Databricks Feature Store or Unity Catalog.

Responsibilities

Design and deploy next best action (NBA) and next best content (NBC) models that select the appropriate action, channel, content, and timing for each customer or healthcare professional (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.
Conduct 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 production deployment.
Design feedback loops that route outcomes back into model retraining.
Apply generative AI and large language models where useful, including content tagging, insight summarization, and content personalization.
Partner with engineering, marketing technology, and business stakeholders.
Explain results to non-technical audiences.
Mentor junior data scientists and establish modeling standards.