Data Science Manager

Beghou brings over three decades of experience helping life sciences companies optimize their commercialization through strategic insight, advanced analytics, and technology. From developing go-to-market strategies and building foundational data analytics infrastructures to leveraging artificial intelligence to improve customer insights and engagement, Beghou helps life sciences companies maximize performance across their portfolios. Beghou also deploys proprietary and third‑party technology solutions to help companies forecast performance, design territories, manage customer data, organize, and report on medical and commercial data, and more.

Headquartered in Evanston, Illinois, we have 10 global offices.

Our mission is to bring together analytical minds and innovative technology to help life sciences companies navigate the complexity of health care and improve patient outcomes.

As a Data Science Manager, you will lead a team of talented data scientists in leveraging advanced analytics, machine learning and Gen AI techniques to extract insights from complex datasets in the pharmaceutical / life sciences domain and effectively communicate the insights to various technical and non‑technical audiences to optimize end outcomes. You will collaborate closely with cross‑functional teams to drive data‑driven decision‑making and shape our product development and commercial delivery.

Additionally, your experience in generative AI will be instrumental in exploring novel life sciences use cases and associated business development.

• Lead and mentor a team of data scientists, providing guidance and support in project execution, methodology selection, and professional development.
• Collaborate with stakeholders to define project objectives, deliverables, and success criteria, ensuring alignment with business goals and priorities.
• Design and implement advanced analytics solutions to address complex challenges in drug discovery, clinical development, and commercialization.
• Apply machine learning techniques, including deep learning and generative AI, to analyze diverse datasets (structured and unstructured) and generate actionable insights.
• Drive innovation by staying abreast of emerging trends, technologies, and best practices in data science and the pharmaceutical industry.
• Establish robust processes for data collection, preparation, and validation, ensuring the reliability and integrity of analytical models and findings.
• Communicate findings and recommendations to technical and non‑technical stakeholders through clear, compelling visualizations, presentations, and reports.
• Collaborate with IT and data engineering teams to optimize data infrastructure and facilitate seamless integration of analytics solutions into existing systems.
• Participate and contribute to thought leadership through active innovation, research and publication.
• Own project management and provide guidance to project teams of junior team members.

• Bachelor's or Master's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics) required;
  Ph.D. preferred.
• 9+ years of experience in data science, with a proven track record of delivering impactful solutions in the pharmaceutical and life sciences industry (Hands‑on experience is preferred)
• Demonstrated success in managing data & analytics engagements in life sciences or healthcare industries. Preferred expertise in analytics related to drug commercialization (sales force analytics, omnichannel, attribution modeling etc.). Analytical Diversity and agility preferred
• Strong proficiency in machine learning, statistical analysis, and data manipulation using Python, or similar state‑of‑the‑art tools. Experience in development and deployment of large‑scale products/solutions is preferred.
• Experience working with cloud platforms such as AWS, Azure, or GCP, including deploying, managing, and optimizing cloud‑based solutions.
• Experience with generative AI development and associated techniques, such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and RL (Reinforcement Learning), preferred.
• Demonstrated…