Associate – Data Science at ProcDNA
Job Description
ProcDNA is a global consulting firm that fuses design thinking with cutting-edge technology to create Commercial Analytics and Technology solutions. With a passionate team of 420+ across 8 offices, ProcDNA has been growing since its launch during the pandemic.
What You’ll Do
– Collect, clean, and process large datasets from multiple sources
– Develop and test advanced machine learning algorithms and models to solve problems in a computationally efficient and statistically effective manner
– Analyze and interpret data to identify trends, patterns, and insights by executing statistical and data mining techniques
– Visualize data and present findings to stakeholders
– Document code, models, and experiments to ensure reproducibility and maintainability
– Collaborate with cross-functional teams to identify business problems and develop data-driven solutions
– Communicate project progress and results to relevant stakeholders
– Stay up to date with the latest data science techniques and tools
Requirements (Must Have)
– Bachelor’s/Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field
– Good understanding of the US pharmaceutical landscape and its key stakeholders
– Hands-on experience or exposure to segmentation, predictive analytics, forecasting, and causal inference modeling use cases
– Proficient in Python with knowledge of libraries such as Pandas, Scikit-Learn, Matplotlib, TensorFlow, etc.; SQL and R are a plus
– Good understanding of data manipulation, statistical analysis, and machine learning concepts
– Ability to work independently and manage multiple tasks simultaneously
– Strong attention to detail, with a research-focused mindset
– Excellent critical thinking and problem-solving skills
– Excellent communication skills – ability to describe findings to both technical and non-technical audiences
– Familiarity with data visualization tools such as Tableau, Power BI, or R Shiny is a plus
– High motivation and good work ethics
