Data Scientist – Pharma Operations Benchmarking

Who You’ll Work With

You will work in our McKinsey Client Capabilities Network office in Wroclaw, as part of a global team of Pharma Operations Benchmarking (POBOS). POBOS offers a portfolio of benchmarking and analytics services that cover multiple areas of pharmaceutical operations value chain such as drug product manufacturing, quality processes, drug substance production or supply chain networks. 

We work with majority of leading pharmaceutical players and their facilities around the world, capturing more than 50 percent of the global installed capacity. Experience and validation of benchmarking insights through on-site diagnostics confirm that POBOS findings are very relevant and fairly assess potential improvement opportunity.

What You’ll Do

In this role, you will provide support to POBOS and our clients serving teams through creating analytical solutions that range from developing and delivering code and models, to digging for meaningful insights in the complex data sets and translating them into actionable plans for our clients. You will help shaping tools tailored to the needs of our clients, utilizing programming languages (e.g., Python), tech platforms and disruptive analytics methodologies as part of our tech-agnostic firm. 

Also, you will have the opportunity to build knowledge of the pharmaceutical industry, derive insights from proprietary information sources and databases and capture existing best practices. Additionally, you will have a chance to create new tools and approaches to overcome the most pressing industry challenges. 

Finally, you will have the opportunity to gain new skills and build on the strengths you bring to the firm. You will receive exceptional training as well as frequent coaching and mentoring from colleagues in your teams. Travelling may be required for up to 10% but is not essential.

Qualifications

• Post-graduate or graduate degree in computer science, engineering, applied mathematics, data science, business analytics or related fields with an excellent academic record
• Experience in machine learning and statistical methods
• Strong knowledge of Python and any data visualization tool (e.g., Tableau, Power BI)
• Strong knowledge of one or more data science or decision science domains (e.g., [un]supervised learning, explainable artificial intelligence, econometrics, deep learning, natural language processing, time series forecasting, deployment, causal inference, uplift modelling)

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