Data Scientist

Job Overview: Data Scientist at ProcDNA

As a Data Scientist at ProcDNA, you will be instrumental in delivering comprehensive data science solutions within pharmaceutical and healthcare contexts. This role requires a unique blend of deep technical expertise, astute business understanding, and a keen scientific curiosity. You will be responsible for transforming complex business challenges into structured analytical frameworks, developing advanced machine learning and statistical models, and generating impactful insights that drive commercial, clinical, and operational value for clients and patients globally.

Beyond technical execution, the Data Scientist is expected to operate strategically, defining business problems, devising robust analytical approaches, and guiding clients toward optimal data-driven decisions.

Key Responsibilities

• Own the full lifecycle of data science projects, from initial problem definition through to deployment, ensuring rigorous methodology, business relevance, and timely execution.
• Design, optimize, and validate advanced machine learning and statistical models, including supervised (classification, regression, uplift), unsupervised (clustering, PCA, GMM), transformer models, and analytical techniques (hypothesis testing, causal inference, survival analysis) using industry-standard 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 or client-facing environments.
• Translate complex insights derived from diverse data sources—such as claims, prescription (LAAD), lab, EMR, and unstructured text—into clear, actionable narratives that inform client decisions across patient, HCP, and market segments.
• Collaborate closely with consultants, domain experts, and engineers to architect analytical workflows addressing intricate commercial or clinical questions.
• Clearly and concisely present findings and insights to both internal teams and client stakeholders.
• Actively engage in client discussions, contributing to solution development and framing the narrative for business audiences.
• Contribute to internal capability development by creating reusable ML assets, accelerators, and documentation, thereby enhancing ProcDNA’s solution portfolio.

Required Skills

• Strong hands-on proficiency in Python, PySpark, and SQL for efficient manipulation and processing of large structured and unstructured datasets.
• Solid theoretical and practical foundation in machine learning algorithms, feature engineering, model tuning, and evaluation methodologies.
• Competence in data visualization tools (e.g., Power BI, Tableau, MS Office suite) and adeptness at effectively communicating analytical results.
• Demonstrated ability to structure ambiguous business problems, formulate strategic analytical roadmaps, and articulate insights to both technical and non-technical audiences.
• Excellent collaboration and project management capabilities for effective coordination within multi-disciplinary teams.

Preferred Skills

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

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related field.
• 4-6 years of professional experience in data science, analytics, or advanced modeling roles.
• Proven capability to balance analytical rigor with deep business understanding, delivering models that are explainable, actionable, and ready for production.

Key skills/competency

• Data Science Solutions
• Machine Learning
• Statistical Modeling
• Python & PySpark
• SQL
• Data Visualization
• Business Acumen
• Causal Inference
• MLOps
• Healthcare Analytics