Data Scientist

Job Overview

As a Data Scientist, 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.

Key Responsibilities

• Own day-to-day execution of data science projects from problem definition to

deployment, ensuring methodological rigor, business relevance, and timely

delivery

• 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

• 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

• 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

• Collaborate with consultants, domain experts, and engineers to structure

analytical workflows that answer complex commercial or clinical questions.

• Present findings and insights to internal and client stakeholders in a clear,

structured, and actionable manner.

• Actively participate in client discussions, supporting solution development and

storyboarding for business audiences

• Contribute to internal capability building through reusable ML assets,

accelerators, and documentation to strengthen the team’s solution portfolio.

Required Skills

• Strong hands-on experience in Python, PySpark, and SQL for manipulating and

handling large structured and unstructured datasets.

• Strong foundation in machine learning algorithms, feature engineering, model

tuning, and evaluation techniques

• Proficiency in data visualization (Power BI, Tableau, MS Office suite or

equivalent) and in translating analytical results effectively.

• Ability to structure ambiguous business problems, design analytical roadmaps,

and communicate insights effectively to both technical and non-technical

stakeholders.

• Strong collaboration and project-management skills for coordinating across

multi-disciplinary teams.

Preferred Skills

• 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).

• Experience with R, Rshiny, and data platforms such as Databricks, AWS, Azure,

or Snowflake is an advantage.

• Exposure to MLOps frameworks, including MLflow, Docker, Airflow, or CI/CD

pipelines, to automate model training, deployment, and monitoring in scalable

production environments.

• Experience mentoring junior analysts or collaborating in cross-functional data

science teams.

• Qualifications

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics,

Data Science, or related fields.

• 4-6 years of professional experience in data science, analytics, or advanced

modeling roles

• Proven ability to balance analytical rigor with business understanding, delivering

models that are explainable, actionable, and production-read