Associate Data Scientist

About ProcDNA ProcDNA is a global consulting firm. We fuse design thinking with cutting-edge tech to create game-changing Commercial Analytics and Technology solutions for our clients. We're a passionate team of 400+ across 8 offices, all growing and learning together since our launch during the pandemic. Here, you won't be stuck in a cubicle - you'll be out in the open water, shaping the future with brilliant minds. At ProcDNA, innovation isn't just encouraged, it's ingrained in our DNA. 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 Must have • Bachelor's/Master’s program 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 and must know about libraries like (Pandas, Scikit-Learn, Matplotlib, TensorFlow, etc.); SQL, R is 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 - should be able to describe findings to a technical and non-technical audience • Familiarity with data visualization tools such as Tableau, Power BI or R Shiny is a plus • High motivation and good work ethic is a must