Senior Data Scientist – Causal Modeling, Experimentation

EXL

. Apply causal inference methods, including propensity score matching, difference-in-differences, synthetic controls, and randomized or quasi-experimental designs, to quantify business impact .

Posted 10/7/2026full-timeJersey City • New Jersey • United StatesSenior💰 $100,000 - $130,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in causal inference methods, experimental design, and statistical analysis to drive data-driven business decisions. Proficient in building predictive models and communicating insights effectively to both technical and non-technical stakeholders.

Highest-signal resume keywords

Causal InferenceExperimental DesignPredictive ModelingPython ProficiencySQL Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Causal InferenceExperimental DesignStatistical TestingRegression AnalysisSampling TechniquesConfidence IntervalsPower AnalysisPredictive ModelingUplift ModelingTreatment-Effect Modeling

Soft Skills

Problem-SolvingStakeholder ManagementCommunication Skills

Tools & Technologies

PythonSQLGCPModeling LibrariesExperimentation Tools

Industry Keywords

Data ScienceAdvanced AnalyticsA/B TestingData PipelinesBusiness Impact

Tech Stack

Tools & technologies

CloudGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

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
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

Requirements

What you’ll need

Bachelor’s or Master’s degree in a quantitative field
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, and GCP
Strong problem-solving, stakeholder management, and communication skills
Preferred: Experience applying causal inference and experimentation in a business setting
Preferred: Familiarity with cloud data platforms, modeling libraries, experimentation tools, and model deployment or monitoring

Benefits

Comp & perks

Bonus
Benefits information provided at https://www.exlservice.com/us-careers-and-benefits