Senior Associate - I&A (Data Science)

What you will do

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.

About Us:

Chryselys is a Pharma Analytics & Business consulting company that delivers data-driven insights leveraging AI-powered, cloud-native platforms to achieve high-impact transformations.We specialize in digital technologies and advanced data science techniques that provide strategic and operational insights.

Role Overview:

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

• • 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.

• Education: Bachelor's or master's 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)
  • 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.