Bachelor's or Master's degree in a quantitative field with 4+ years of relevant experience
Strong knowledge of causal inference and experimental design
Hands-on experience with predictive and treatment-effect modeling
Proficiency in Python, SQL, and GCP
Excellent problem-solving and communication skills
Responsibilities
Apply causal inference methods to quantify business impact
Build predictive and treatment-effect models for improved targeting
Design and analyze A/B tests, including statistical testing
Analyze large datasets and develop reproducible analytical frameworks
Collaborate with cross-functional teams to communicate findings
Benefits
Hybrid work environment in NYC
Opportunity to work on impactful data-driven solutions
Collaboration with cross-functional teams
Focus on advanced analytics and experimentation
Potential for performance-based bonuses
Full Job Description
Job Description
Senior Data Scientist
Hybrid, NYC
$100-130k plus bonus
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.
Responsibilities
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
Qualifications
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
About EXL Service
EXL Service is a leading operations management and analytics company that helps businesses enhance growth and profitability. The company provides services in areas such as finance and accounting, customer service, and healthcare. EXL Service was founded in 1999 and is headquartered in New York, New York.
