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 420+ 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
Skills: machine learning,models,data science
