Data Scientist

As a Data Scientist - I at ProcDNA, you will be instrumental in implementing comprehensive data science solutions for pharmaceutical and healthcare clients. This role requires a strong combination of technical expertise, business understanding, and scientific inquiry. You will be responsible for transforming intricate business challenges into analytical frameworks, developing robust machine learning and statistical models, and extracting actionable insights to positively impact commercial, clinical, and operational outcomes for clients and patients globally. Strategic thinking is essential; you will frame business issues, design analytical strategies, and guide clients toward informed, data-driven decisions.

Responsibilities:

• Oversee the complete lifecycle of data science projects, from defining problems to deploying solutions, ensuring methodological accuracy, business relevance, and timely completion.
• Construct, optimize, and validate sophisticated machine learning and statistical models, including supervised (classification, regression, uplift) and unsupervised (clustering, PCA, GMM) techniques, transformer models, and analytical approaches (hypothesis testing, causal inference, survival analysis) using standard industry libraries.
• Produce clean, modular, and production-ready code with reusable components, adhering to best practices in version control, documentation, and scalable pipeline design for production and client-facing deployments.
• Consolidate insights from diverse data sources such as claims, prescription (LAAD), lab, EMR, and unstructured text into compelling narratives that inform client decisions, considering patient, HCP, and market contexts.
• Collaborate with consultants, subject matter experts, and engineers to establish analytical workflows that address complex commercial or clinical inquiries.
• Present findings and insights to internal teams and clients in a clear, structured, and actionable manner.
• Actively engage in client discussions, contributing to solution development and business storytelling.
• Enhance internal capabilities by developing reusable ML assets, accelerators, and documentation to enrich the team's solution offerings.

Required Skills:

• Proficiency in Python, PySpark, and SQL for managing and analyzing large structured and unstructured datasets.
• Solid understanding of machine learning algorithms, feature engineering, model optimization, and evaluation methods.
• Expertise in data visualization tools (Power BI, Tableau, MS Office suite, or equivalent) and in effectively communicating analytical outcomes.
• Ability to define ambiguous business problems, create analytical roadmaps, and articulate insights to both technical and non-technical audiences.
• Strong collaboration and project management skills to facilitate effective teamwork across multidisciplinary groups.

Preferred Skills:

• Previous experience in the pharmaceutical or life sciences sector, with familiarity with data sources like LAAD, Lab, Sales, and unstructured data (market research, physician notes, publications).
• Experience with R, Rshiny, and data platforms such as Databricks, AWS, Azure, or Snowflake.
• Exposure to MLOps frameworks (MLflow, Docker, Airflow, CI/CD pipelines) for automating model training, deployment, and monitoring in scalable production environments.
• Experience mentoring junior analysts or working in cross-functional data science teams.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related field.
• 1-3 years of professional experience in data science, analytics, or advanced modeling roles.
• Demonstrated ability to balance analytical rigor with business acumen, delivering models that are explainable, actionable, and production-ready.