About ProcDNA

ProcDNA is a global consulting firm. We fuse design thinking with cutting-edge technology to create game-changing Commercial Analytics and Technology solutions for our clients. We're a passionate team of 275+ across 6 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. Ready to join our epic growth journey.

What are we looking for?

As a Data Scientist- I, you will play a key role in delivering end-to-end data science solutions across pharmaceutical and healthcare engagements. The role demands a blend of technical depth, business acumen, and scientific curiosity. You will translate complex business problems into analytical frameworks, develop scalable machine learning and statistical solutions, and generate actionable insights that drive commercial, clinical, and operational impact for clients and patients worldwide. Beyond technical execution, you are expected to think strategically, framing business challenges, designing analytical approaches, and guiding clients toward the most effective data-driven decisions.

What you’ll do

1. Own day-to-day execution of data science projects from problem definition to deployment, ensuring methodological rigor, business relevance, and timely delivery
2. Build, tune, and validate advanced machine learning and statistical models, including supervised techniques (classification, regression, uplift), unsupervised methods (clustering, PCA, GMM), transformer models, and analytical frameworks (hypothesis testing, causal inference, survival analysis) using industry-standard libraries
3. Develop clean, modular, and production-ready code with reusable components, adhering to best practices in version control, documentation, and scalable pipeline design for deployment in production or client-facing environments
4. Synthesize insights from diverse data sources, including claims, prescription (LAAD), lab, EMR, and unstructured text, into clear narratives driving client decisions tailored to patient, HCP, and market contexts
5. Collaborate with consultants, domain experts, and engineers to structure analytical workflows that answer complex commercial or clinical questions.
6. Present findings and insights to internal and client stakeholders in a clear, structured, and actionable manner.

Must have

1. Strong hands-on experience in Python, PySpark, and SQL for manipulating and handling large structured and unstructured datasets.
2. Strong foundation in machine learning algorithms, feature engineering, model tuning, and evaluation techniques
3. Proficiency in data visualization (Power BI, Tableau, MS Office suite or equivalent) and in translating analytical results effectively.
4. Ability to structure ambiguous business problems, design analytical roadmaps, and communicate insights effectively to both technical and non-technical stakeholders.
5. Strong collaboration and project-management skills for coordinating across multi-disciplinary teams.

Preferred Skills

1. Prior experience in the pharmaceutical or life sciences industry, with familiarity across structured data sources such as LAAD, Lab, Sales, and unstructured datasets (e.g., market research, physician notes, publications).
2. Experience with R, Rshiny, and data platforms such as Databricks, AWS, Azure, or Snowflake is an advantage.
3. Exposure to MLOps frameworks, including MLflow, Docker, Airflow, or CI/CD pipelines, to automate model training, deployment, and monitoring in scalable production environments.
4. Experience mentoring junior analysts or collaborating in cross-functional data science teams.

Qualifications

1. Bachelor’s or master’s degree in computer science, Statistics, Mathematics, Data Science, or related fields.
2. 3–4 years of professional experience in data science, analytics, or advanced modeling roles
3. Proven ability to balance analytical rigor with business understanding, delivering models that are explainable, actionable, and production-ready