Data Science II

New Era Solutions; Bengaluru; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

A global life sciences consulting firm is looking for a Data Scientist II in Bangalore. This role involves leading end-to-end data science projects and building advanced machine learning models. The ideal candidate will have a strong foundation in Python, ML algorithms, and experience in the pharmaceutical sector. You will work collaboratively with various teams to deliver actionable insights. This position offers a dynamic environment focused on growth and innovation.

Qualifications

• 3.5 - 6 years of experience in data science, preferably in life sciences.
• Strong hands-on Python, PySpark, and SQL skills.
• Experience in building and tuning machine learning models.

Responsibilities

• Lead end-to-end execution of data science projects.
• Develop and validate advanced machine learning models.
• Communicate insights to stakeholders.

Skills

Python

PySpark

SQL

Machine learning algorithms

Data visualization (Power BI, Tableau)

R/RShiny

MLOps tools (MLflow, Docker, Airflow)

Collaboration skills

Job description

Job Title: Data Scientist II - Bangalore

Location: Bangalore

Experience: 3.5 - 6 Years

ProcDNA is a global rocket ship in life sciences consulting. 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 400+ 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. Ready to join our epic growth journey?

What we are looking for

As a Data Scientist II, 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

• Lead end-to-end execution of data science projects from problem definition to deployment, ensuring rigor, business relevance, and timely delivery
• Build, tune, and validate advanced ML and statistical models (classification, regression, uplift, clustering, PCA, GMM, transformers) and analytical frameworks (hypothesis testing, causal inference, survival analysis)
• Develop clean, modular, production-ready code following best practices in version control, documentation, and scalable pipeline design
• Translate insights from diverse data sources (claims, prescription/LAAD, lab, EMR, unstructured text) into actionable narratives for patient, HCP, and market decisions
• Collaborate with consultants, domain experts, and engineers to design analytical workflows for complex commercial and clinical problems
• Communicate insights clearly to internal and client stakeholders; support client discussions, solution design, and storyboarding
• Contribute to internal capability building through reusable ML assets, accelerators, and documentation

Must have

• 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
• Experience in pharma or life sciences, working with structured data (LAAD, lab, sales) and unstructured sources (market research, physician notes, publications)
• Proficiency in R/RShiny and exposure to modern data platforms (Databricks, AWS, Azure, Snowflake)
• Hands‑on experience with MLOps tools (MLflow, Docker, Airflow, CI/CD) for scalable model training, deployment, and monitoring
• Experience mentoring junior analysts and collaborating within cross‑functional data science teams