Data Scientist

Key Responsibilities:

• Apply advanced forecasting methodologies to estimate sales and revenue for newly launched and pipeline pharmaceutical products (Demand Forecasting good to have skills).
• Incorporate financial modelling and corporate finance principles into forecasting frameworks to support long-term strategic planning.
• Develop robust, data-driven models that incorporate market dynamics, patient behaviour, and competitive intelligence to generate accurate and actionable forecasts.
• Collaborate cross-functionally with marketing, commercial, and research teams to ensure alignment of forecasts with strategic objectives and product positioning.
• Leverage statistical, machine learning, and deep learning techniques to uncover insights that inform launch strategies and optimize product performance.
• Partner with key business stakeholders to identify, scope, and execute predictive analytics initiatives that address critical business needs and deliver measurable value.
• Maintain a strong understanding of the pharmaceutical landscape from financial operations to commercialization To provide objective, data-driven input into strategic decisions.
• Collaborate within the analytics team, openly sharing knowledge and seeking feedback to enhance the quality and impact of analytical deliverables.
• Stay current with emerging techniques and tools in predictive analytics, ensuring methodological rigor and adaptability in approach.
• Work comfortably with varied data sources and types, ensuring seamless integration and analysis across structured and unstructured datasets.

Requirements:

• Minimum of 5 years of working experience as a Data Scientist in industry.
• Deep and broad knowledge of Data Science methods and tools including Simulation model building principles, Machine Learning, and best practices:
• Point Forecasting and Probabilistic Forecasting.
• Statistical Forecasting: ARIMA, THETA, Exponential Smoothing
• ML-based Forecasting: GBM, LightGBM, XGBoost, Linear Regression
• DL-based Forecasting: DeepAR, NBEATS, LSTM, RNN
• Hierarchical Forecasting
• Cross-Validation: Rolling-window, Expanding-window, K-fold, Hold-out
• MLOps is also good to have skills.
• Any type of simulation model experience (Good to have).
• Agent-based Simulation Model using Anylogic (Good to have).
• Exposure to LLM or GPT good to have skills

Key Skills: Data Scientist, Machine Learning, Statistical Forecasting, Python