<div>At Arohana Tech Solutions, we are driven by a mission to elevate businesses through cutting-edge IT solutions. With a focus on innovation, scalability, and reliability, we provide tailored technology services that empower organizations to achieve their goals in an ever-evolving digital landscape. Our team of experts specializes in delivering comprehensive IT solutions, from strategy and implementation to ongoing support, ensuring that our clients stay ahead of the curve. At Arohana, we don’t just build solutions—we build partnerships for long-term success.</div>

Arohana Tech Solutions Private Limited

Job Description – Senior Data Scientist (Experimentation &amp; Causal Inference)Overview
We are seeking an experienced Senior Data Scientist specializing in Experimentation and Causal Inference to lead the design, execution, and analysis of large-scale experimentation initiatives. The ideal candidate will have deep expertise in statistical modeling, A/B testing, causal inference methodologies, and advanced analytics to drive data-informed business decisions. This role partners closely with business leaders, product teams, and engineering organizations to deliver actionable insights that influence strategic direction.
Key ResponsibilitiesExperimentation &amp; Statistical Analysis
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Lead end-to-end experimentation initiatives, including A/B testing and Geo Experiments, from hypothesis development through execution, analysis, and business recommendations.
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Design statistically rigorous experiments and ensure appropriate experimental methodologies are applied.
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Perform sample size calculations, statistical power analysis, and minimum detectable effect (MDE) estimation.
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Manage multiple testing scenarios while applying appropriate false discovery rate (FDR) control techniques.
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Apply both Bayesian and Frequentist statistical approaches where appropriate.
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Causal Inference &amp; Advanced Analytics
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Develop and apply advanced causal inference methodologies, including:
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Difference-in-Differences (DiD)
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Propensity Score Matching
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Synthetic Controls
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Instrumental Variables
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Doubly Robust Estimation
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Meta Learners
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Uplift Modeling
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Validate assumptions underlying causal inference techniques and ensure statistical rigor throughout analyses.
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Build predictive and inferential models using regression, classification, time series forecasting, and other advanced statistical techniques.
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Scalable Data Science Solutions
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Design and develop reusable experimentation and causal inference frameworks that can scale across multiple business domains.
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Build statistical analysis packages and reusable Python/R libraries to improve analytical efficiency.
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Develop production-ready analytical workflows and automation.
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Business Partnership &amp; Strategic Insights
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Collaborate with business stakeholders to identify optimization opportunities through experimentation and advanced analytics.
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Translate complex statistical findings into clear, actionable business recommendations.
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Present analytical insights to senior leadership and executive stakeholders in an easily understandable manner.
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Influence strategic decisions through data-driven storytelling.
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Leadership &amp; Mentorship
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Mentor junior data scientists and analysts on statistical methods, experimentation best practices, and scalable solution development.
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Promote a culture of innovation, experimentation, quality, and continuous improvement.
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Lead cross-functional initiatives involving analytics, engineering, and business teams.
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Required QualificationsEducation
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Bachelor's degree in one of the following disciplines:
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Statistics
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Economics
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Computer Science
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Engineering
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Mathematics
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Physics
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Or another related quantitative field
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Experience
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Minimum 7+ years of experience with a strong focus on experimentation, statistical modeling, and causal inference.
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Technical SkillsStatistical Modeling
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Regression Analysis
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Classification Models
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Time Series Forecasting
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Experimental Design
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Statistical Inference
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Causal Inference
Strong expertise in:
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Propensity Score Methods
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Synthetic Controls
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Difference-in-Differences (DiD)
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Instrumental Variables
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Doubly Robust Estimation
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Meta Learners
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Uplift Modeling
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Experimentation
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A/B Test Design
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Geo Experiments
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Statistical Power Analysis
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Sample Size Estimation
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Minimum Detectable Effect (MDE)
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Multiple Testing Corrections
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False Discovery Rate (FDR) Control
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Bayesian Statistics
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Frequentist Statistics
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Programming
Strong proficiency in:
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Python
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R
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Experience with:
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scikit-learn
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LightGBM (LGBM)
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Statistical analysis package development
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Communication &amp; Business Skills
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Excellent written and verbal communication skills.
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Ability to simplify complex analytical concepts for technical and non-technical audiences.
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Experience presenting recommendations to senior executives.
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Strong stakeholder management and cross-functional collaboration skills.
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Proven ability to independently lead analytical initiatives from concept to implementation.
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Preferred QualificationsEducation
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Master's degree in Computer Science, Statistics, Mathematics, or another quantitative discipline with 5+ years of relevant experience
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OR
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PhD in a quantitative discipline with 3+ years of relevant experience in experimentation or causal inference.
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Preferred Technical ExperienceData Engineering
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ETL Development
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Data Extraction
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Data Transformation
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Data Integration
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Data Quality Management
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Production &amp; MLOps
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CI/CD Pipelines
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Production Deployment
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Model Monitoring
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Experiment Monitoring
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Automated Reporting
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Data Platforms &amp; Tools
Experience with:
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Databricks
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Jupyter Notebook
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Snowflake
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GitHub
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Business Acumen
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Experience supporting subscription-based business models is highly desirable.
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Strong understanding of customer analytics, market trends, and consumer behavior.
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Ability to connect analytical findings with business strategy and commercial outcomes.
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Leadership
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Demonstrated experience leading cross-functional projects.
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Strong stakeholder and project management skills.
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Ability to adapt quickly in fast-paced environments while maintaining high-quality deliverables.
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Proven experience mentoring data science teams and promoting engineering and analytical best practices.
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