Job description
Tiger Analytics is at the forefront of leveraging AI and advanced analytics to tackle some of the most complex challenges faced by global organizations. We build tailored, data-driven solutions for several Fortune 100 companies, with a strong presence across the US, UK, India, Singapore, and a globally distributed remote team.
We are currently seeking a Data Professional with a strong foundation in data analytics and hands-on experience in demand forecasting and resource allocation within the pharmaceutical and life sciences sector. The ideal candidate will also possess robust coding skills and a practical understanding of how analytics can drive strategic decisions in pharma operations
Key Responsibilities:
Effectively communicate forecasting strategies and methodologies to cross-functional stakeholders, ensuring alignment with business objectives across teams.
Promote a culture of data-driven decision-making, educating stakeholders on the how and why behind demand forecasts, scenario planning, and probabilistic modeling to drive adoption and trust.
Led the development and implementation of advanced forecasting models, collaborating with data engineers, therapeutic area leads, supply planners, and commercial analytics teams to ensure solutions were practical, integrated, and impactful.
Design and deploy scalable, transparent, and dynamic forecasting models that can adapt to changes in market dynamics, regulatory shifts, or product life cycle stages.
Foster a collaborative learning environment by mentoring junior analysts and engaging with other data scientists to continuously evolve best practices in pharmaceutical forecasting.
Engage with external communities and industry conferences to stay current on innovations in statistical and time-series modeling, causal inference, and AI/ML applications in pharma.
Respond to regulatory and investor demands with innovative forecasting solutions that provide clarity, defensibility, and foresight using cutting-edge analytics tools and statistical techniques.
Graduate degree in Business Analytics, MBA, or equivalent work experience in a relevant quantitative field
10–16 years of professional experience, with at least 7 years in data analytics and forecasting in the pharma/life sciences domain
Strong expertise in time-series forecasting, including seasonality adjustments and use of exogenous variables (e.g., macroeconomic and marketing data)
Hands-on experience working with pharmaceutical data sources such as Veeva, IQVIA (PlanTrak, LAAD, PE), and RWD sources like TriNetX, Flatiron, Optum, and Komodo
Experience applying advanced data science techniques to solve commercial and strategic problems in pharma
Strong foundation in statistics and econometrics, with practical experience using statistical tools in Python or SAS
Proven ability to design, build, and scale interpretable forecasting models for both short-term and long-term planning
Proficiency in data visualization using Tableau, including dashboard development, publishing, and automation
Strong programming and data manipulation skills in Python and SQL, with the ability to write complex queries
Excellent communication and stakeholder management skills, with the ability to translate analytics into actionable business insights.
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.
