What are the responsibilities and job description for the Data Scientist position at Arohana Tech Solutions Private Limited?
Job Description – Senior Data Scientist (Experimentation & 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 & Statistical Analysis
Strong Expertise In:
Strong Proficiency In:
Experience With:
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 & Statistical Analysis
- Lead end-to-end experimentation initiatives, including A/B testing and Geo Experiments, from hypothesis development through execution, analysis, and business recommendations.
- Design statistically rigorous experiments and ensure appropriate experimental methodologies are applied.
- Perform sample size calculations, statistical power analysis, and minimum detectable effect (MDE) estimation.
- Manage multiple testing scenarios while applying appropriate false discovery rate (FDR) control techniques.
- Apply both Bayesian and Frequentist statistical approaches where appropriate.
- Develop and apply advanced causal inference methodologies, including:
- Difference-in-Differences (DiD)
- Propensity Score Matching
- Synthetic Controls
- Instrumental Variables
- Doubly Robust Estimation
- Meta Learners
- Uplift Modeling
- Validate assumptions underlying causal inference techniques and ensure statistical rigor throughout analyses.
- Build predictive and inferential models using regression, classification, time series forecasting, and other advanced statistical techniques.
- Design and develop reusable experimentation and causal inference frameworks that can scale across multiple business domains.
- Build statistical analysis packages and reusable Python/R libraries to improve analytical efficiency.
- Develop production-ready analytical workflows and automation.
- Collaborate with business stakeholders to identify optimization opportunities through experimentation and advanced analytics.
- Translate complex statistical findings into clear, actionable business recommendations.
- Present analytical insights to senior leadership and executive stakeholders in an easily understandable manner.
- Influence strategic decisions through data-driven storytelling.
- Mentor junior data scientists and analysts on statistical methods, experimentation best practices, and scalable solution development.
- Promote a culture of innovation, experimentation, quality, and continuous improvement.
- Lead cross-functional initiatives involving analytics, engineering, and business teams.
- Bachelor's degree in one of the following disciplines:
- Statistics
- Economics
- Computer Science
- Engineering
- Mathematics
- Physics
- Or another related quantitative field
- Minimum 7 years of experience with a strong focus on experimentation, statistical modeling, and causal inference.
- Regression Analysis
- Classification Models
- Time Series Forecasting
- Experimental Design
- Statistical Inference
Strong Expertise In:
- Propensity Score Methods
- Synthetic Controls
- Difference-in-Differences (DiD)
- Instrumental Variables
- Doubly Robust Estimation
- Meta Learners
- Uplift Modeling
- A/B Test Design
- Geo Experiments
- Statistical Power Analysis
- Sample Size Estimation
- Minimum Detectable Effect (MDE)
- Multiple Testing Corrections
- False Discovery Rate (FDR) Control
- Bayesian Statistics
- Frequentist Statistics
Strong Proficiency In:
- Python
- R
- scikit-learn
- LightGBM (LGBM)
- Statistical analysis package development
- Excellent written and verbal communication skills.
- Ability to simplify complex analytical concepts for technical and non-technical audiences.
- Experience presenting recommendations to senior executives.
- Strong stakeholder management and cross-functional collaboration skills.
- Proven ability to independently lead analytical initiatives from concept to implementation.
- Master's degree in Computer Science, Statistics, Mathematics, or another quantitative discipline with 5 years of relevant experience
- PhD in a quantitative discipline with 3 years of relevant experience in experimentation or causal inference.
- ETL Development
- Data Extraction
- Data Transformation
- Data Integration
- Data Quality Management
- CI/CD Pipelines
- Production Deployment
- Model Monitoring
- Experiment Monitoring
- Automated Reporting
Experience With:
- Databricks
- Jupyter Notebook
- Snowflake
- GitHub
- Experience supporting subscription-based business models is highly desirable.
- Strong understanding of customer analytics, market trends, and consumer behavior.
- Ability to connect analytical findings with business strategy and commercial outcomes.
- Demonstrated experience leading cross-functional projects.
- Strong stakeholder and project management skills.
- Ability to adapt quickly in fast-paced environments while maintaining high-quality deliverables.
- Proven experience mentoring data science teams and promoting engineering and analytical best practices.
Salary : $125,000 - $140,000