Senior Data Scientist
Hybrid, NYC
$100-130k plus bonus
For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits
The posted range is the hiring range for this role - a subset of the broader range available to employees over time - and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate''s skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.
We are seeking a highly skilled Sr. Data Scientist with strong expertise in causal inference, experimentation, predictive modeling, and advanced analytics. The role will focus on developing data-driven solutions to understand customer or business behavior, identify incremental impact, optimize targeting strategies, and measure the effectiveness of business interventions.
The ideal candidate will have a strong foundation in statistical modeling and experimental design, with the ability to translate complex analytical findings into actionable business recommendations. The role requires experience working with large datasets, developing scalable analytical solutions, and partnering with cross-functional stakeholders to solve complex business problems.
Required Qualifications
- Bachelor's or Master's degree in a quantitative field and 4+ years of relevant data science or advanced analytics experience
- Strong knowledge of causal inference, experimental design, statistical testing, regression, sampling, confidence intervals, and power analysis
- Hands-on experience with predictive, uplift, or treatment-effect modeling and machine learning evaluation
- Proficiency in Python, SQL & GCP
- Strong problem-solving, stakeholder management, and communication skills
Preferred Qualifications
- Experience applying causal inference and experimentation in a business setting
- Familiarity with cloud data platforms, modeling libraries, experimentation tools, and model deployment or monitoring
Key Responsibilities
- Apply causal inference methods-including propensity score matching, difference-in-differences, synthetic controls, and randomized or quasi-experimental designs-to quantify business impact
- Build predictive, uplift, and treatment-effect models to improve targeting, prioritization, and resource allocation
- Design and analyze A/B tests, including sample sizing, control and treatment groups, statistical testing, and segment-level effects
- Analyze large datasets, develop reproducible analytical frameworks, and collaborate on scalable data pipelines
- Partner with cross-functional teams and communicate findings, recommendations, and business impact to technical and non-technical stakeholders
Success Measures
- Deliver reliable, scalable models and experiments that quantify incremental impact and improve business decisions
- Optimize targeting and resource allocation through statistically rigorous analysis
- Clearly communicate insights and recommendations across business and technical teams
Key Responsibilities
- Apply causal inference methods-including propensity score matching, difference-in-differences, synthetic controls, and randomized or quasi-experimental designs-to quantify business impact
- Build predictive, uplift, and treatment-effect models to improve targeting, prioritization, and resource allocation
- Design and analyze A/B tests, including sample sizing, control and treatment groups, statistical testing, and segment-level effects
- Analyze large datasets, develop reproducible analytical frameworks, and collaborate on scalable data pipelines
- Partner with cross-functional teams and communicate findings, recommendations, and business impact to technical and non-technical stakeholders
Success Measures
- Deliver reliable, scalable models and experiments that quantify incremental impact and improve business decisions
- Optimize targeting and resource allocation through statistically rigorous analysis
- Clearly communicate insights and recommendations across business and technical teams