Data Scientist

Responsibilities:

• Develop predictive and descriptive analytical models to support commercial business objectives.
• Analyze large-scale healthcare, pharmaceutical, sales, claims, prescription (Rx), patient, and market data to uncover business insights.
• Build machine learning and statistical models for customer segmentation, targeting, forecasting, next best action, and campaign effectiveness.
• Design and implement data pipelines, feature engineering, and model validation processes.
• Collaborate with commercial stakeholders to translate business problems into analytical solutions.
• Create dashboards and visualizations to communicate findings effectively using BI tools.
• Monitor model performance and recommend improvements based on business feedback.
• Work with cross-functional teams including data engineering, business consulting, and commercial excellence teams.
• Ensure data quality, documentation, and adherence to data governance standards.

Requirements:

• Bachelor's or master's degree in data science, statistics, computer science, mathematics, engineering, or a related quantitative discipline.
• 2-7 years of experience in data science with a focus on pharma or life sciences commercial analytics.
• Strong understanding of pharmaceutical commercial processes, including sales analytics, marketing analytics, customer analytics, and market performance measurement.
• Hands-on experience with machine learning algorithms, statistical analysis, and predictive modeling.
• Proficiency in Python or R for data analysis and model development.
• Strong SQL skills for data extraction and manipulation.
• Experience with data visualization tools such as Tableau or Power BI.
• Familiarity with cloud platforms (AWS, Azure, or GCP) is preferred.
• Understanding of Agile methodologies and version control tools such as Git is an advantage.
• Technical Skills: Python, SQL, R (preferred), Machine Learning, Statistical Modeling, Data Visualization (Tableau/Power BI), Spark or PySpark (preferred), Databricks (preferred), Cloud Platforms (AWS, Azure, or GCP).

Soft Skills:

• Strong analytical and problem-solving abilities.
• Excellent communication and stakeholder management skills.
• Ability to present complex analytical findings to non-technical audiences.
• Strong collaboration and teamwork skills.
• Self-driven with the ability to manage multiple priorities.