Job description

About Tiger Analytics Shape 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) • 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 Why Join Tiger Analytics: • Drive High-Impact Business Transformation: Lead strategic engagements that go beyond traditional analytics, delivering enterprise-wide AI adoption, cloud-native data modernization, and measurable business outcomes. • Build and Lead at Global Scale: Collaborate with exceptional AI, data, engineering, and consulting talent across geographies to deliver transformative solutions and grow strategic client partnerships. • Benefits: Medical, Dental, Vision, Retirement Accounts, Long and Short Term Disability, Life Insurance, HSA/FSA Accounts (USA) and Discretionary time-off. • Continuous Learning & AI Upskilling: Stay ahead of the rapidly evolving AI landscape through continuous learning, hands-on exposure to emerging technologies, and opportunities to build expertise in Generative AI, agentic AI, and next-generation data platforms. Grow alongside a team that invests in developing future-ready skills and encourages innovation. 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. Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.

Requirements

Must-haves

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
6 – 8 years in data science or applied ML, with hands-on NBA, NBC, recommendation, or personalization work in production
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
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
Drive High-Impact Business Transformation: Lead strategic engagements that go beyond traditional analytics, delivering enterprise-wide AI adoption, cloud-native data modernization, and measurable business outcomes.
Build and Lead at Global Scale: Collaborate with exceptional AI, data, engineering, and consulting talent across geographies to deliver transformative solutions and grow strategic client partnerships.

Nice-to-haves

Strong Databricks experience (Spark/PySpark, Delta Lake, MLflow; Feature Store or Unity Catalog a plus)
Bachelor's or Master's in a quantitative field (Master's or PhD preferred)
Preferred (Not Required)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningpythonsqlci/cdgo

Platform: workable
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision