6-8 years of experience in data science or applied machine learning, focusing on NBA, NBC, or personalization in production environments.
Strong expertise in Databricks, including Spark/PySpark, Delta Lake, and MLflow; familiarity with Feature Store or Unity Catalog is a plus.
Solid foundation in machine learning and deep learning frameworks like PyTorch or TensorFlow, along with reinforcement learning techniques.
Proficient in Python and SQL for data manipulation and model development.
Experience in deploying models using MLOps practices, including CI/CD and monitoring.
Knowledge of experimentation methodologies, A/B testing, and measuring incremental impact.
Bachelor's or Master's degree in a quantitative field; a Master's or PhD is preferred.
Responsibilities
Design and deploy NBA/NBC models to optimize customer interactions.
Build machine learning, deep learning, and reinforcement learning solutions, including contextual bandits and sequential decision models.
Develop models for propensity, response, and uplift to enhance decision-making.
Conduct offline policy evaluations and online experiments to assess model effectiveness.
Manage Databricks training pipelines, including feature engineering and automated evaluation gates.
Create feedback loops to ensure outcomes inform model retraining.
Utilize GenAI/LLMs for tasks like content tagging and personalization.
Collaborate with engineering and marketing teams, translating complex results for non-technical stakeholders.
Mentor junior data scientists and establish modeling standards.
Benefits
Significant career development opportunities in a fast-growing environment.
High degree of individual responsibility in a challenging role.
Full Job Description
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)
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
Benefits
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.
About Tiger Analytics
Tiger Analytics is a consulting firm that provides data analytics consulting services to businesses. The company specializes in data science, machine learning, and artificial intelligence. Tiger Analytics helps businesses to leverage data to make better decisions, improve operations, and drive growth. The company has worked with clients in a variety of industries, including healthcare, retail, finance, and technology.
