Lead Data Scientist- Pharma Marketing

Tiger Analytics Inc.•Jersey City, NJ

About The Position

Tiger Analytics is looking for experienced Data Scientists to join our fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world. As a Lead Data Scientist you will be at the forefront of solving high-impact business problems using advanced machine learning, data engineering, and analytics solutions. The role demands a balanced mix of technical expertise, stakeholder management, and leadership. You will collaborate with cross-functional teams and business partners to define the technical problem statement and hypotheses to test. You will develop efficient and accurate analytical models which mimic business decisions and incorporate those models into analytical data products and tools. You will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.

Requirements

8+ years of relevant Data Science experience with a deep focus on US Pharmaceutical Marketing.
Campaign Optimization: Proven track record in optimizing non-personalized, multichannel, and Omnichannel (HCP/Patient) marketing strategies.
Journey Analytics: Deep understanding of Patient & Customer Journey mapping, media performance attribution, and behavioral segmentation.
Advanced Analytics: Expertise in foundational ML (Regression, Classification, Optimization) with a nuanced understanding of statistical assumptions and limitations.
Production-Grade Code: Proficiency in writing modular, scalable, and bug-free Python.
The Data Stack: High proficiency in SQL and experience navigating Big Data environments (Spark, Hive, or Hadoop).
MLOps & Cloud: Hands-on experience with version control (Git), containerization (Docker), and cloud ecosystems (AWS, Azure, or GCP)
Stakeholder Influence: Ability to lead high-stakes analytics engagements and translate complex data findings into "so-what" insights for senior leadership.
Communication: Exceptional presentation skills, capable of driving strategic conversations and building consensus across diverse organizational teams.
Growth Mindset: A proactive hunger to learn emerging technologies and adapt to the evolving healthcare data landscape.

Responsibilities

Analytical Translation: Translate complex business problems into sophisticated analytical structures, conceptualising solutions anchored in statistical and machine learning methodologies.
Problem Solving: While technical proficiency in data manipulation, statistical modelling, and machine learning is crucial, the ability to apply these skills to solve real-world business problems is equally vital.
Client Engagement: Establish a deep understanding of clients; business contexts, working closely to unravel intricate challenges and opportunities.
Algorithmic Expertise: Develop and refine algorithms and models, sculpting them into powerful tools to surmount intricate business challenges.
Quantitative Mastery: Conduct in-depth quantitative analyses, navigating vast datasets to extract meaningful insights that drive informed decision-making.
Cross-Functional Collaboration: Collaborate seamlessly with multiple teams, including Consulting and Engineering, fostering relationships with diverse stakeholders to meet deadlines and bring Analytical Solutions to life

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