Senior Associate - I&A (Data Science)

Chryselys; Hyderabad; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

Chryselys is seeking a Senior Associate - Insights & Analytics (Data Science) in Hyderabad. The role focuses on causal analytics, lift measurement, and impact attribution beyond traditional predictive modeling.

The candidate should have 2–5 years of data science experience in pharma/healthcare, with strong Python/R skills and familiarity with SQL, cloud environments, and data visualization tools. This is a full-time on-site position in Hyderabad.

Qualifications

• Bachelor's or master's in a quantitative field with strong academic record.
• 2–5 years of data science experience in pharma/healthcare, with Sales/Claims/Payer data.
• Proficiency in Python and R; familiarity with TensorFlow, Scikit-learn, and Pandas.

Responsibilities

• Lead causal analytics and uplift modeling for commercial measurement.
• Design and execute experiments, including A/B testing and power analysis.
• Apply quasi-experimental methods (DiD, synthetic controls, PSM, RDD) to real-world data.
• Communicate insights clearly to non-technical stakeholders and drive data-driven decisions.

Skills

Causal Analytics

Lift Measurement

Impact Attribution

A/B Testing

Experimental Design

Power Calculations

Minimum Detectable Effect

Observational Causal Inference

Uplift Modeling

Python

R

Education

Bachelor's or Master's in data science / statistics / computer science

Tools

statsmodels

scipy.stats

CausalPy

DoWhy

Job description

Role Overview

We re Hiring: Senior Associate - Insights & Analytics (Data Science) at Chryselys

Location: Hyderabad

Department: Insights & Analytics

Job Type: Full-time

Reports To: Manager

Role Overview:

We are looking for a Data Science specialist who can hit the ground running immediately in commercial measurement and causal analytics. The ideal candidate must go beyond standard machine learning modeling to demonstrate expertise in experimental design, statistical measurement, and observational causal inference.

Key Technical Requirements & Skills

• Core Focus: Causal Analytics, Lift Measurement, and Impact Attribution (rather than pure Predictive Machine Learning).
• Experimental Design & A/B Testing:
• Statistical power calculations, sample size determination, and Minimum Detectable Effect (MDE).
• In-depth understanding of A/B test mechanics and variance reduction techniques.
• Quasi-Experimental & Observational Causal Inference:
• Difference-in-Differences (DiD)
• Synthetic Controls
• Propensity Score Matching (PSM)
• Regression Discontinuity Designs (RDD)
• Uplift & Behavioral Attribution:
• Demonstrated experience proving that a specific model, campaign, or feature drove a true incrementality/change in user or customer behavior.
• Hands-on experience with uplift modeling techniques.
• Technical Stack:
• Strong proficiency in Python, specifically statistical packages such as statsmodels, scipy.stats, CausalPy, or DoWhy

What You Bring

• Education: Bachelors or masters degree in data science, statistics, computer science, engineering, or a related quantitative field with a strong academic record.
• Experience: 2-5 years of experience in data science, particularly in the pharmaceutical or healthcare industry, working with key datasets like Sales, Claims, and Payer data.
• Skills:
  • Proficiency in programming languages such as Python and R, with a deep understanding of libraries like TensorFlow, Scikit-learn, and Pandas.
  • Strong experience with SQL and cloud-based data processing environments such as AWS (Redshift, Athena, S3), and experience with Jupyter Notebooks/SageMaker.
  • Demonstrated ability to build data visualizations and communicate insights through tools like PowerBI, Tableau, Qlik, QuickSight, or similar.
  • Strong analytical skills, with experience in hypothesis testing, A/B testing, and statistical analysis.
  • Ability to manage multiple projects, prioritize tasks, and meet deadlines in a fast-paced environment.
  • Excellent communication and presentation skills, with the ability to explain complex data science concepts to non-technical stakeholders.
  • A strong problem-solving mindset, with the ability to adapt and innovate in a dynamic consulting environment.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.